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Problem with interpolating a vertical profile in 3D Analyst (ArcMap 10.1)

Problem with interpolating a vertical profile in 3D Analyst (ArcMap 10.1)


I have a problem when I want to create a vertical profile in 3D Analyst from a DEM file. I want to interpolate the data and show points from 20 to 20 meters, as I specified in the option panel (see figure 1). After I press the button to show the Profile Graph, the X-axis do not show values between 20 meters as I specified in the option panel (see figure 2). What can I do to solve this issue?

Note: the Y-axis is displayed correctly in terms of altitude. Datum: WGS_1984


Vince's comment partially answers your problem - your data is in degrees, so the x axis or length is also going to be displayed in degrees rather than meters.

However I think you're also confusing what Profile Sampling actually does. It will not change the values or labels of your x axis - that's the length of your line in whatever units of measure your CRS is. When you specify a value for profile sampling, that means in addition to getting an elevation value for every vertex in your profile line, it will also get an elevation value every 20m along that line. The purpose is to increase the vertical resolution of (smooth out) the profile line.


Abstract

Glacier reconstructions are widely used in palaeoclimatic studies and this paper presents a new semi-automated method for generating glacier reconstructions: GlaRe, is a toolbox coded in Python and operating in ArcGIS. This toolbox provides tools to generate the ice thickness from the bed topography along a palaeoglacier flowline applying the standard flow law for ice, and generates the 3D surface of the palaeoglacier using multiple interpolation methods. The toolbox performance has been evaluated using two extant glaciers, an icefield and a cirque/valley glacier from which the subglacial topography is known, using the basic reconstruction routine in GlaRe. Results in terms of ice surface, ice extent and equilibrium line altitude show excellent agreement that confirms the robustness of this procedure in the reconstruction of palaeoglaciers from glacial landforms such as frontal moraines.


References

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Bijesh, C.M., Kurian, P.J., Yatheesh, V., Tyagi, A., & Twinkle, D (2018). Morphotectonic characteristics, distribution and probable genesis of bathymetric highs off southwest coast of India. Geomorphology, 315

Chai H, Guan W, Wan X, Li X, Zhao Q, Liu S (2018) A wave power device with pendulum based on ocean monitoring buoy. Iop Conf Ser Earth Environ Sci 108:052013

Chenyang D, Kun W, Kang LK (2018) Landscape pattern dynamic change research of Harbin Songbei based on GIS technology. IOP Conference Series Earth and Environmental Science, p 153

Collister BL, Zimmerman RC, Sukenik CI, Hill VJ, Balch WM (2018) Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar. Remote Sens Environ 215:85–96

Dai D, Wang X, Long J, Meng T, Zhu G, Zhang J (2017) Feature extraction of GIS partial discharge signal based on s-transform and singular value decomposition. Iet Sci Measur Technol 11(2):186–193

Huan, Q., & Yaguang, L.I.(2017).Gis-based spatial distribution characteristics of settlements and its optimization in the tropical lingshui county of Hainan province. Science of Soil&Water Conservation

Kim H, Soerensen AL, Jin H, Heimbürger LE, Hahm D, Rhee TS et al (2017) Methylmercury mass budgets and distribution characteristics in the western pacific ocean, Environmental Science& Technology. 51(3):1186–1194

Kim W, Yi JH, Min IK, Shim JS (2018) Monitoring of structural dynamic characteristics of Socheongcho ocean research station. J Coast Res 85:1191–1195

Li, Z., Jia, C., & Da, H.(2017a). Research on characteristics of ionospheric echo based on ocean. IEEE Advanced Information Management, Communicates, Electronic& Automation Control Conference

Li, C., Zhang, Z., Zhao, W., & Tian, J.(2017b). A statistical study on the subthermocline submesoscale eddies in the northwestern pacific ocean based on argo data. Journal of Geophysical Research, 122

Ling, J.(2017). Zaozhuang tourism resource management system research based on GIS. International Conference on Information Management

Liu Y, Deng Y, Fang M, Li D, Wu D (2017) Research on the torque characteristics of a seawater hydraulic axial piston motor in deep-sea environment. Ocean Eng 146:411–423

Reglero P, Santos M, Balbín R, Laíz-Carrión R, Alvarez-Berastegui D, Ciannelli L et al (2017) Environmental and biological characteristics of Atlantic bluefin tuna and albacore spawning habitats based on their egg distributions. Deep Sea Res Part II Top Stud Oceanogr 140:S0967064517300875

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Sun, L.J., Xuexu, H.U., Wei, L.U., & Wang, B.J.(2018). Spatial distribution characteristics of wheat grain quality and analysis of factors based on GIS. Scientia Agricultura Sinica

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Modelling the vulnerability of groundwater to contamination in an unconfined alluvial aquifer in Pakistan

The area of Thal Doab is located in the Indus Basin and is underlain by a thick alluvial aquifer called the Thal Doab aquifer (TDA). The TDA is undergone intense hydrological stress owing to rapid population growth and excessive groundwater use for livestock and irrigated agricultural land uses. The potential impact of these land uses on groundwater quality was assessed using a DRASTIC model in a Geographic Information System environment. Seven DRASTIC thematic maps were developed at fixed scale and then combined into a groundwater vulnerability map. The resultant vulnerability index values were grouped into four zones as low, moderate, high and very high. The study has established that 76% of the land area that is underlain by the TDA has a high to very high vulnerability to groundwater contamination mainly because of a thin soil profile, a shallow water table and the presence of soils and sediments with high hydraulic conductivity values. In addition, only 2 and 22% of the total area lie in low and moderate vulnerability zones, respectively. The outcomes of this study can be used to improve the sustainability of the groundwater resource through proper land-use management.

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10-m Bathymetry grid of Vineyard and western Nantucket Sounds produced from lead-line and single-beam sonar soundings, swath-interferometric, multibeam, and lidar datasets (Esri binary grid, UTM Zone 19N, WGS84)

Baldwin, Wayne, 2016, 10-m Bathymetry grid of Vineyard and western Nantucket Sounds produced from lead-line and single-beam sonar soundings, swath-interferometric, multibeam, and lidar datasets (Esri binary grid, UTM Zone 19N, WGS84): Open-File Report 2016-1119, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

Online Links:

Baldwin, Wayne E. , Foster, David S. , Pendleton, Elizabeth A. , Barnhardt, Walter A. , Schwab, William C. , Andrews, Brian D. , and Ackerman, Seth D. , 2016, Shallow Geology, Sea-Floor Texture, and Physiographic Zones of Vineyard and western Nantucket Sounds, Massachusetts: Open-File Report 2016-1119, U.S. Geological Survey, Reston, VA.

Online Links:

West_Bounding_Coordinate: -71.035443 East_Bounding_Coordinate: -70.428111 North_Bounding_Coordinate: 41.561895 South_Bounding_Coordinate: 41.319300

Beginning_Date: 1938 Ending_Date: 31-Aug-2011 Currentness_Reference: ground condition

Geospatial_Data_Presentation_Form: raster digital data

Grid_Coordinate_System_Name: Universal Transverse Mercator Universal_Transverse_Mercator: UTM_Zone_Number: 19 Transverse_Mercator: Scale_Factor_at_Central_Meridian: 0.999600 Longitude_of_Central_Meridian: -69.000000 Latitude_of_Projection_Origin: 0.000000 False_Easting: 500000.000000 False_Northing: 0.000000

Planar coordinates are encoded using row and column
Abscissae (x-coordinates) are specified to the nearest 10.000000
Ordinates (y-coordinates) are specified to the nearest 10.000000
Planar coordinates are specified in meters

The horizontal datum used is D_WGS_1984.
The ellipsoid used is WGS_1984.
The semi-major axis of the ellipsoid used is 6378137.000000.
The flattening of the ellipsoid used is 1/298.257224.

Entity_and_Attribute_Overview: There are no attributes associated with this Esri ArcRaster Grid. Pixel values represent bathymetric depths in meters referenced to the North American Vertical Datum of 1988, and range from -0.1 to -32.8 m. Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Why was the data set created?

How was the data set created?

Poppe and others, 2007 (source 1 of 10)

Poppe, L.J., Ackerman, S.D., Foster, D.S., Blackwood, D.S., Butman, B., Moser, M.S., and Stewart, H.F., 2007, Sea-floor character and surface processes in the vicinity of Quicks Hole, Elizabeth Islands, Massachusetts: Open-File Report 2006-1357, U.S. Geological Survey, Reston, VA.

Online Links:

Poppe and others, 2010 (source 2 of 10)

Poppe, L.J., McMullen, K.Y., Foster, D.S., Blackwood, D.S., Williams, S.J., Ackerman, S.D., Moser, M.S., and Glomb, K.A., 2010, Geological interpretation of the sea floor offshore of Edgartown, Massachusetts: Open-File Report 2009-1001, U.S. Geological Survey, Reston, VA.

Online Links:

Pendleton and others, 2012 (source 3 of 10)

Pendleton, E.A., Twichell, D.C., Foster, D.S., Worley, C.R, Irwin, B.J., and Danforth, W.W., 2012, High-resolution geophysical data from the sea floor surrounding the Western Elizabeth Islands, Massachusetts: Open-File Report 2011-1184, U.S. Geological Survey, Reston, VA.

Online Links:

Andrews and others, 2014 (source 4 of 10)

Andrews, B.D., Ackerman, S.D., Baldwin, W.E., Foster, D.S., and Schwab, W.C., 2014, High-Resolution Geophysical Data from the Inner Continental Shelf: Vineyard Sound, Massachusetts: Open-File Report 2012-1006, U.S. Geological Survey, Reston, VA.

Online Links:

Pendleton and others, 2014 (source 5 of 10)

Pendleton, E.A., Andrews, B.D., Danforth, W.W., and Foster, D.S., 2014, High-resolution geophysical data collected aboard the U.S. Geological Survey research vessel Rafael to supplement existing datasets from Buzzards Bay and Vineyard Sound, Massachusetts: Open-File Report 2013-1020, U.S. Geological Survey, Reston, VA.

Online Links:

Poppe and others, 2008 (source 6 of 10)

Poppe, L.J., McMullen, K.Y., Foster, D.S., Blackwood, D.S., Williams, S.J., Ackerman, S.D., Barnum, S.R., and Brennan, R.T., 2008, Sea-floor character and sedimentary processes in the vicinity of Woods Hole, Massachusetts: Open File Report 2008-1004, U.S. Geological Survey, Reston, VA.

Online Links:

NOAA, 2008 (source 7 of 10)

National Oceanic and Atmospheric Administration, and Survey, National Ocean , 2008, Descriptive report, navigable area survey H11920, Vineyard Sound, Massachusetts, Gay Head to Cedar Tree Neck: Descriptive Report H11920, National Oceanographic and Atmospheric Administration - National Ocean Survey, Norfolk, VA.

Online Links:

NOAA, 2008 (source 8 of 10)

National Oceanic and Atmospheric Administration, and Survey, National Ocean , 2008, Descriptive report, navigable area survey H11921, Vineyard Sound, Massachusetts, Sow and Pigs reef to Quicks Hole: Descriptive Report H11921, National Oceanographic and Atmospheric Administration - National Ocean Survey, Norfolk, VA.

Online Links:

USACE-JABLTCX, 2009 (source 9 of 10)

U.S. Army Corps of Engineers - Joint Airborne Lidar Bathymetry Center of Expertise, 2009, 2005 - 2007 US Army Corps of Engineers (USACE) Topo/Bathy Lidar: Maine, Massachusetts, and Rhode Island: NOAA National Ocean Service (NOS), Coastal Services Center (CSC), Charleston, SC.

Online Links:

NOAA Single-Beam Soundings (source 10 of 10)

NOAA National Geophysical Data Center, 2015, NOS Hydrographic Survey Data.

Online Links:

Date: 2014 (process 1 of 6) Collected and prepared non-USGS source datasets: NOAA data from NOS hydrographic surveys in the 1900s (lead-line and single-beam soundings) were downloaded from the NGDC website individually (<http://maps.ngdc.noaa.gov/viewers/bathymetry/>) in XYZ ASCII format the data are referenced to Geographic NAD83 and Mean Lower Low Water. Data from NOAA NOS hydrographic surveys conducted since 2000 were also downloaded from the NGDC website. The 2000 era data were in either XYZ or BAG format, and referenced to UTM Zone 19N, and Mean Lower Low Water. The following NOAA NOS survey data were used: 1930s - H06348, H06349, H06350, H06468 1940's - H06533 1950s - H08170 1960s - H08820, H08821, H08902, H08903, H08904 1970s - H09645, H09646 2000s - H11920, H11921. Datasets obtained in BAG format were imported into Fledermaus (v7.4.0) then exported to XYZ text format. USACE JALBTCX lidar data were obtained online from the NOAA Coastal Services Center Digital Coast Data Access Viewer, which provided an ftp download of a subset of the bathymetric lidar data within a defined area of interest. The data were delivered in XYZ format, and referenced to Geographic NAD83 and NAVD88. XYZ coordinates referenced to NAD 83 were projected to UTM Zone 19N using proj (v4.8.0).

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Date: 2014 (process 2 of 6) Collected and prepared USGS source datasets: For swath bathymetry data sources from previously published USGS Open File Reports (including 2006-1357, 2008-1004, 2009-1001, 2012-1006, 2011-1184, and 2013-1020), XYZ formatted text files were exported from the same Caris Base surfaces used to export Esri format grids for publications. The Esri format grids can be downloaded from the publications listed in the Source Citations. They are referenced to UTM Zone 19N, and Mean Lower Low Water.

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

  • Poppe and others, 2007 Poppe and others, 2008 Poppe and others, 2010 Pendleton and others, 2012 Andrews and others, 2013 Pendleton and others, 2014

Date: 2014 (process 3 of 6) Using NOAA VDatum (v3.2), all of the source datasets referenced to tidal datums were converted from MLLW to NAVD88 vertical datum

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Date: 2014 (process 4 of 6) Evaluated source data overlap and created subsets for contribution to composite surface: Overlap of the swath bathymetry and lidar datasets was evaluated using polygon feature classes representing the areal outlines of the published grids and points. Point feature retention was determined by the following preferential hierarchy 1) USGS swath-interferometric data (2012-1006, 2011-1184, and 2013-1020), 2) NOAA-NOS multibeam data (from USGS Open File Reports 2006-1357, 2008-1004, 2009-1001, and NOS Descriptive Reports H11920, H11921), 3) USACE-JABLTCX Lidar, and 4) NOS leadline and single-beam. Polygon outlines of higher-ranked source datasets were used to spatially select coincident points from lower-ranked XYZ feature datasets and delete them. After redundant source XYZ feature data were eliminated, the remaining subsets were imported to an ArcGIS (v9.3) geodatabase as point feature classes.

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Date: 2014 (process 5 of 6) An ArcGIS (9.3.1) Terrain Dataset was created (ArcToolbox > 3D Analyst Tools > Terrain > Create Terrain), all of the contributing point feature datasets and a polygon feature encompassing the input points (used as a hard clip) were added to the terrain (ArcToolbox > 3D Analyst Tools > Terrain > Add Feature Class to Terrain), and the terrain was constructed (ArcToolbox > 3D Analyst Tools > Terrain > Build Terrain).

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Date: 2014 (process 6 of 6) The composite bathymetry was converted from the Terrain Dataset to a 10 meter per pixel, floating point Esri Grid (vns10m_navd88) using the natural neighbors interpolation method (ArcToolbox > 3D Analyst Tools > Conversion > From Terrain > Terrain to Raster). During the conversion, the polygon area encompassing the input points was specified to clip the extent of the output grid (Environmental Settings > Raster Analysis Settings > Mask)

Person who carried out this activity:

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

U.S. Geological Survey, 2015, Geologic Mapping of the Seafloor Offshore of Massachusetts: U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

Online Links:

How reliable are the data what problems remain in the data set?

The horizontal accuracy of the source multibeam bathymetry (DGPS) is reported at +/- 3 meters. The horizontal accuracy of the swath bathymetry (DGPS and/or RTK-GPS) is conservatively estimated to be +/- 10 meters. The lidar data were collected to meet the +/- 3-meter accuracy specification. Horizontal accuracy for lead-line and single-beam sonar soundings are not documented. This composite topographic and bathymetric grid is assumed to be horizontally accurate to within 20 meters, as additional errors could have been introduced during resampling, vertical and horizontal reprojections, and interpolation. See USGS Open File Reports and NOAA survey descriptive reports for individual survey positioning methods.

The vertical accuracy for NOAA lead-line and single-beam sonar soundings are not documented. NOAA survey methods and processing of sounding data are in descriptive reports. All XYZ sounding data downloaded from NGDC are in tenths of meters. The vertical accuracy of the source multibeam bathymetry (RESON SeaBat 8101) is reported at +/- 30 to 50 cm. The vertical accuracy of the swath bathymetry is estimated to be +/- 10 to 60 cm. The lidar data were collected to meet the +/- 30-cm accuracy specification. This composite topographic and bathymetric grid is assumed to be vertically accurate to within 1-meter, as additional errors could have been introduced during resampling, vertical and horizontal reprojections, and interpolation.

This grid includes lead-line soundings, single-beam sonar, swath interferometric, multibeam, and lidar data collected during several surveys in Vineyard and western Nantucket Sounds. The input grids can be downloaded by visiting the publications listed in the Source Information.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Access_Constraints: None Use_Constraints: Not to be used for navigation. Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey (USGS) as the source of this information.

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

vns10m_navd88.zip from USGS Open File report 2016-1119. WinZip V. 14.5 file contains bathymetry from Vineyard and western Nantucket Sounds, MA and the associated metadata.

    Availability in digital form:

Data format: WinZip V. 14.5 file contains bathymetry from Vineyard and western Nantucket Sounds, MA and the associated metadata in format AIG (version ArcMAP 9.3.1) Esri grid Size: 16.61
Network links: <https://pubs.usgs.gov/of/2016/1119/GIS_catalog/SourceData/bathy/vns10m_navd88.zip>
<https://pubs.usgs.gov/of/2016/1119/ofr2016-1119-data_catalog.html>

Who wrote the metadata?

508-548-8700 x2226 (voice)
508-457-2310 (FAX)
[email protected]

Generated by mp version 2.9.12 on Fri Aug 19 11:42:48 2016


Geospatial-based soil variability and hydrological zones of Abha semi-arid mountainous watershed, Saudi Arabia

Soil and water resources are vital to a society because these resources maintain the environment and living being. Understanding the inconsistencies of these resources has significant importance for regional planning and landscape management. Though their relevance, there is no updated data and organized method for characterization and mapping of soil database and hydrological system in Abha watershed area. The objective of this study is to develop a soil geodatabase and generation of hydrological zones. The methodology is based on the Geoinformatics techniques to determine the causative factors that affect the hydrology and to delineate hydrological zones. Ten parameters were considered to delineate the hydrological zones based on the literature review and thorough discussion with international scientific experts (i.e., engineers, hydrologists, and hydrogeologists). The themes and their classes were assigned suitable weights on Saaty’s scale according to their relative importance. The assigned weights of the themes and their classes were normalized by analytic hierarchy process and eigenvector method. Thereafter, the all themes were integrated in geographical information system (GIS) using weighted linear combination method to create the hydrological map. Thus, five hydrological zones were identified and demarcated in the study area, viz. “very low runoff,” “low runoff,” “moderate runoff,” “high runoff,” and “very high runoff” based on surface runoff potential index values. This analysis also shows that 5.09 and 32.29 % of the watershed lies in the very high runoff and high runoff zone, followed by moderate and low runoff zone with 28.17 and 25.22 % areal coverage, respectively. With this qualitative-based classification, the high runoff generation area of the Abha watershed has a large area coverage, indicating the availability of high surface water potential. Additionally, the results showed that the runoff distribution in the western, southwestern, and central north highlands of the watershed is high and these areas flow to the down wadies. Thus, the contribution of this work in understanding the Abha watershed hydrologic system is significant. The results provide valuable information of Abha watershed about the hydrological zone and soil database at a large scale for the first time. Generated maps will assist to formulate effective runoff utilization plans so as to ensure long-term sustainability.

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Problem with interpolating a vertical profile in 3D Analyst (ArcMap 10.1) - Geographic Information Systems

GIS tools for erosion/deposition modeling and multidimensional visualization

Part IV: Processed based erosion simulation for spatially complex conditions and its applications to installations

Part V: Impact of transport capacity and terrain structures on erosion simulations

Geographic Modeling and Systems Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois

H. Mitasova, L. Mitas, W.M. Brown, D. Johnston

U.S.Army Construction Engineering Research Laboratories

Urbana, November 1997, http://www2.gis.uiuc.edu:2280/modviz/reports/cerl97/rep97.html

Contents

2.1 Unit stream power based erosion/deposition model (USPED). 4

2.2 Process-based simulation of water erosion (SIMWE). 7

2.2.1 Impact of spatially uniform parameters in complex terrain. 7

2.2.2 Transport and detachment capacity equations (V). 10

2.2.3 Impact of spatially variable land cover (V). 11

2.2.4 Impact of temporal variability in land cover. 12

2.2.5 Simulation of impact of terrain structures. 14

4. Applications to military installations. 16

5. Conclusion, future directions. 25

7. Full size figures (for printed version). 30

Note: Chapters (V) describe work performed for the PART V contract, the rest is for the PART IV contract.

1. Introduction

To fulfill the need for better understanding of spatial and temporal distributions of phenomena resulting from landscape processes, empirical models based on lumped, averaged parameters are being replaced by process-based distributed models supported by Geographic Information Systems (GIS) (Moore et al. 1993, Maidment 1996, Engel 1995, Vieux et al. 1996, Saghafian 1996). Over the last few years there has been a remarkable progress in development of tools for studies of hydrological processes represented, e.g., by the CASC2d model (Saghafian 1996) and others (Garrote and Brass 1995, Vieux 1996). While these developments enhanced the capabilities to estimate overland flow in complex terrain, the development of distributed, process-based simulations of erosion, sediment transport and deposition at similar levels of realism has been slower, due to the enormous complexity of processes involved. Most of the current process-based erosion models can be roughly divided into three main groups:

a) field scale models developed for soil conservation (e.g., WEPP) which are based on a 1D flow over hillslope segments (Foster et al. 1995). They incorporate the impact of soil, cover and management practices with a great detail, however, the description of topography is very simplified. Data preparation for larger and complex fields can be time consuming and results are given only for profiles or as statistical averages or integrals for entire hillslopes or small watersheds.

b) watershed scale models designed for water quality applications focus on predicting sediment loads and concentrations of chemicals delivered to and transported through streams using channel routing (e.g., Arnold et al. 1993, Srinivasan and Arnold 1994, Rewerts and Engel 1991). The overland flow related processes are modeled using sets of homogeneous hillslope segments or subwatersheds with lumped parameters.

c) landscape scale models simulating the landscape evolution by geomorphological processes which describe the changes in topography at the temporal scales from hundreds to thousands of years (e.g., Kirkby 1986, Willgoose et al. 1991, Kramer and Marder 1992, Howard 1994).

Although this categorization is hardly exhaustive and there are several models which combine more than one of the above given concepts, a number of issues important for effective erosion prevention at a landscape scale remain unresolved. Many existing methods require large number of empirical inputs, important effects of terrain shape are not taken fully into account, numerical methods used for the model implementations exhibit instabilities when used at high resolutions, and detailed spatially variable outputs are limited. Combination of these factors often make applications of traditional models either laborious or simply not practical, especially for large areas. Some of the key unsolved modeling issues which restrict the use of the current models to increase our knowledge about spatio-temporal aspects of erosion processes and limit their application to effective erosion prevention for large, complex areas can be identified as follows:

a) high resolution treatment of complex terrain, land use and soils in a fully 2D/3D distributed manner,

b) description of processes at a fundamental level with minimal number of empirical inputs and availability of robust and efficient process solvers to minimize tedious manual modifications of data,

c) integration of models at different spatial and temporal scales,

d) validation of both spatial and quantitative aspects model of predictions. While the capabilities to qualitatively predict some spatial aspects of erosion and deposition improved (Mitas and Mitasova 1998a), the quantitative estimates are still very problematic (e.g., Bjorneberg et al. 1997 reports soil loss predictions from WEPP of 200 kg/m when measured soil loss was less than 5 kg/m , Johnson et al., 1997 reports several results from estimation of sediment yield by CASC2D-SED with 100-400% differences between the computed and observed yields). Closer correspondence between experiments and models is needed.

e) low quality of available digital elevation models which require sophisticated re-processing or manual modifications to make them usable for process modeling.

f) development of soil and cover parameters for process-based models requires an ongoing extensive experimental work, while the relationships between these parameters is not very well understood.

g) providing numerical, graphical and cartographic outputs in a form which enables easy spatial analysis and supports decision-making process.

It is clear that resolving these complex issues will require considerable time and effort of many research teams, therefore in this project we focus on a selected subset of the problems, especially those which we believe are relevant to erosion modeling at military installations. In this report, we focus on the most recent enhancements of our models, we elucidate the role of input parameters and illustrate the a use of simulations for land use management. In applications part we demonstrate and discuss the technical issues related to routine applications of erosion simulations to military installations.

2. Methods

The methods and GIS tools which we have developed to support modeling of erosion, sediment transport and deposition in complex terrain are described in the previous reports (Mitasova et al. 1995, 1996ab) and publications (Mitasova and Mitas 1993, Mitas and Hofierka 1993, Mitasova et al. 1995, Mitasova et al. 1996, Mitas and Mitasova 1997, Mitas et al. 1997, Mitas and Mitasova 1998a,b)). In this report we briefly recall the principles of these methods and focus on the most recent developments.

2.1 Unit stream power based erosion/deposition model (USPED)

The original version of the USPED model is described in Mitasova et al. (1996ab), therefore here we only briefly recall its principles. USPED is a simple model which predicts the spatial distribution of erosion and deposition rates for a steady state overland flow with uniform rainfall excess conditions for transport capacity limited case of erosion process. Sediment flow rate is approximated by sediment transport capacity T ( r ), r = (x,y) which is computed as a power function of slope ß( r ) , upslope area A ( r ) and transportability coefficient K ( r ) dependent on soil and cover. Net erosion and deposition is computed as a change in sediment flow rate. In this report we compare the original formulation of USPED (Mitasova et al 1996a) with an improved version derived as a special case of the SIMWE model (Mitas and Mitasova 1998a).

Improved impact of topography . Within the original USPED the water and sediment flow is modeled as 1D flow along a flow line generated over 3D terrain. The net erosion/deposition rate is computed as a change in the sediment flow rate along the flow line, approximated by a directional derivative of the sediment flow rate. For this univariate case, the net erosion/deposition rate D( r ) is

where r =(x,y) , s(r) is the unit vector in the steepest slope direction, h ( r ) is the water depth estimated from the upslope area A ( r ), k p (r) is the profile curvature (terrain curvature in the direction of the minus gradient, i.e., the direction of the steepest slope). This 1D flow-based formulation includes the impact of water flow, slope and profile curvature, however, the impact of tangential curvature is incorporated only through the water flow term. The predicted pattern is in a good agreement with observations except for heads of valleys where it predicts only erosion while the soil maps and field experiments indicate that both deposition and erosion is observed, and for shoulders where it does not predict commonly reported local maximum in erosion rates (Martz 1987, 1991, Sutherland 1991, Quinne et al. 1994).

Within the 2D flow formulation, which was derived as a special case of the more general model SIMWE (Mitas and Mitasova 1998a) we represent the water an sediment flow as a bivariate vector field q(r) = q ( x,y ), q s ( r )= q s ( x,y ). Then, the net erosion/deposition rate is estimated as a divergence of the sediment flow. If we assume uniform rainfall, soil and cover conditions, and a transport capacity limiting case with sediment flow close to sediment transport capacity, the net erosion/deposition can be written as (see Appendix in Mitas and Mitasova 1998a):

where k t ( r ) is the tangential curvature (curvature in the direction perpendicular to the gradient, i.e., the direction tangential to a contourline projected to the normal plane). Topographic parameters s(r) , k p ( r ), k t ( r ) are computed from the first and second order derivatives of terrain surface approximated by the regularized spline with tension (RST) (Mitasova and Mitas, 1993 Mitasova and Hofierka, 1993 Krcho 1991). According to (2), the spatial distribution of erosion/deposition is controlled by the change in the overland flow depth (first term) and by the local geometry of terrain (second term), including both profile and tangential curvatures. The equation (2) thus demonstrates that the local acceleration of flow in both the gradient and tangential directions (related to the profile and tangential curvatures) play equally important roles in spatial distribution of erosion/deposition. The impact of the tangential curvature k t ( r ) is therefore twofold. First, k t ( r ) influences the water depth h ( r ) through its control of water flow convergence/divergence, with tangential concavity leading to rapid increase in water depth and an increase in the potential for erosion (first term in (2)). Second, k t ( r ) causes a local change in sediment flow velocity which for tangential concavity has an opposite effect (reduction in sediment transport), creating thus potential for deposition. The interplay between the magnitude of water flow change and both terrain curvatures reflected in the equation (2) therefore determines whether erosion or deposition will occur.

When the results of the 1D flow (Figure 1c) and the 2D flow (Figure 1b) models are compared with the observed pattern of colluvial deposits (Figure 1a), it is clear, that equation (1) fails to predict deposition observed in areas where profile curvature is close to zero but there is a significant tangential concavity (areas A and B in Figures 1abc). It also underestimates erosion in areas with tangential convexity (shoulders) . The prediction by the 2D flow model in these areas is in significantly better agreement with the observed pattern of deposition. The 1D flow model underestimates the overall extent of deposition as only a 18% of the total area, while the 2D flow model, without taking into account the spatial variability in surface cover, predicts deposition at 26% of the total area. Colluvial deposits thicker than 10cm, indicating long term deposition, were observed in 40% of the sampling sites. The sedimentation area observed on a steep hillslope covered by grass, which contributes to the spatial extent of deposition (Figure 1a) cannot be predicted by using only the elevation data and the incorporation of spatially variable cover is necessary.

Figure 1. Full incorporation of topography for transport capacity limited erosion : a) observed depths of colluvial deposits, b) erosion and deposition pattern from 2D flow formulation of USPED, c) erosion and deposition pattern from 1D flow formulation of USPED, d) water flow term, e) profile curvature term, f) tangential curvature term.

The 2D flow equation (2) also provides a sound theoretical explanation for the results of field experiments reported by several authors, e.g., Busacca et al., (1993) and Sutherland, (1991) where `the highest erosion was observed on divergent shoulder elements and deposition on convergent footslope elements', as well as by Heimsath et al., (1997), or Quine et al. (1994) observing `maximum soil loss from the slope convexities and maximum gain in both the slope concavities and the main thalwegs'.

Computer implementation of the more complete USPED equation (2) is rather simple, using the tools already developed for this project. Water depth h = A i n / sqrt ( sin ß)**0.6 is estimated from upslope contributing area A computed from a DEM using r.flow program (Mitasova et al. 1996b), while topographic parameters (slope ß , aspect and curvatures) are computed by interpolation program s.surf.rst ( Mitasova et al. 1995b ), r.resamp.rst (Mitasova et al. 1996b) or by enhanced r.slope.aspect (Appendix) . The i is rainfall excess and n is Mannings roughness coefficient. The equation for h and the equation (2) for net erosion/deposition D ( r ) are then computed using r.mapcalc .

Influence of rainfall, soil and cover properties . Soil and cover parameters similar to those used in USLE or WEPP were not developed for the USPED model, as no systematic experimental work was performed. However, it was suggested (Moore and Wilson 1992) that under certain conditions, there is a relation between the USPED concept and USLE. It is therefore possible to combine the model with the USLE/RUSLE factors to predict the relative impact of land use change on erosion/deposition pattern assuming the conditions valid for USPED hold. This approach was used e.g., for the Camp Shelby study (Mitasova et al., 1996b). Caution should be used when interpreting the results because the USLE parameters were developed for simple plane fields and to obtain accurate predictions for complex terrain conditions they need to be re-calibrated ( Foster 1990, Mitasova et al 1997 reply).

2.2 Process-based simulation of water erosion (SIMWE)

The SIMWE model is described in the previous report (Mitasova et al. 1996c) and in Mitas and Mitasova (1998a), therefore we present here only a brief concept and focus on the role of the parameters. SIMWE is a landscape scale, bivariate model of erosion and deposition by overland flow designed for spatially complex terrain, soil and cover conditions. The underlying continuity equations are solved by Green's function Monte Carlo method, to provide robustness necessary for spatially variable conditions and high resolutions. We use the experimental farm data described in Mitasova et al. (1995b, 1996b) and by (Auerswald et al. 1996), to explain the role of the parameters and to elucidate how natural phenomena and properties influence the erosion process.

2.2.1 Impact of spatially uniform parameters in complex terrain

Meyer and Wischmeier (1969) presented an erosion model based on principles later formulated as a closed form erosion equation by Foster and Meyer (1972) and used in the WEPP, CREAMS and numerous other models (Hong and Mostaghimi 1995, Haan et al. 1994) including SIMWE. They formulated the model for a 1D complex profile and analyzed its behavior for various combination of parameters, elucidating thus the impact of different terrain, rainfall, soil and cover properties on distribution of erosion and deposition rates along a profile. The following examples represent a generalization of this analysis to terrain represented by bivariate (2D) function in a 3D space.

In this analysis the baseline situation for all examples is 36mm/hour rainfall intensity, full saturation, (therefore rainfall intensity = rainfall excess rate), rough surface (e.g., dense grass with Mannings n = 0.1 ), sandy type of soil (negligible critical shear stress, with low detachability (erodibility) and low transportability due to the dense cover and larger particle size: K t = K d = 0.0003). The following examples illustrate how the magnitude and pattern of erosion/deposition rates changes due to the change in one of the parameters.

Rainfall excess is estimated as rainfall intensity - infiltration rate where infiltration rate can be estimated from a separate module or using r.mapcalc based on the soil data. Rainfall excess influences the magnitude of erosion/deposition rates, with increasing rainfall excess both the erosion and deposition rates increase, however the spatial pattern of erosion and deposition does not change (Figure 2).

Figure 2. Impact of increasing rainfall excess a) r = 36 mm/h, b) r = 72 mm/h, c) r = 144 mm/h.

Surface roughness, represented in our case by the Manning's coefficient, influences water and sediment flow velocities. Surface roughness parameter depends on vegetation cover as well as the soil surface and its values for variety of situations have been derived from experiments and are available from literature and the WEPP users manual. The change in surface roughness changes the erosion and deposition pattern. In our example, the extent of deposition for smooth surfaces n=0.01 is predicted only for locations with strong profile and tangential concavity covering about 14% of the total area while for rough surfaces with n=0.1 the extent of deposition is closer to the transport capacity limiting case, covering more than 24% of total area (Figure 3).

Figure 3. Impact of surface roughness a) n=0.01 (smooth surface), b) n=0.10 (rough surface).

Critical shear stress represents the soils resistance to the shearing forces of water flow. It depends on soil and cover properties and the values are available e.g., from the WEPP manual (Flanagan and Nearing, 1995). If shear stress at the given location is lower than the critical shear stress, no soil is detached. This parameter therefore has an impact on erosion/deposition pattern. Its high value reduces the spatial extent of erosion, on the other hand, it can also increase the magnitude of erosion rates on steeper hillslopes and in areas with concentrated flow due to the fact that clean water has higher potential to transport the sediment (Figure 4).

Figure 4 . Impact of uniform critical shear stress a) 1.0, b)3.0, c) 7.0.

Erodibility (detachment capacity coefficient) is a measure of soil susceptibility to detachment by water flow (Flanagan and Nearing, 1995). It is often defined as the increase in soil detachment per unit increase in shear stress of clear water flow. Change in erodibility factor while keeping the other parameters constant changes the ratio between transport capacity and detachment capacity . This leads to the change in the character of erosion process from detachment capacity limited for erodibility significantly lower than transportability to transport capacity limited case when erodibility is greater than transportability. Therefore, as shown in the animation (Figure 5), change in erodibility will change the spatial pattern of erosion and deposition, however the impact on the magnitude of the sediment load in stream is small. This example is a good illustration of the fact that measuring sediment load in the watershed outlet does not provide enough information for understanding the erosion processes on watershed's hillslopes and quite different processes can result in the same sediment concentration levels. This makes the use of instream sediment concentrations as a measure reflecting the impact of erosion protection measurers needed for land use management problematic.

F igure 5. (animation) Impact of change in soil erodibility Kd=(0.0001,0.1), Kt=0.001 .

Transportability or transport capacity coefficient is a measure of soil capability to be transported by water flow. It depends on soil properties but can be also influenced by vegetation. This coefficient is not directly measured and provided in the WEPP, rather it is estimated indirectly, which is making the proper determination of this parameter problematic. However, the parameter can be derived at least for some types of soils using the published values of first order reaction coefficient or using the procedure suggested by Finkner et al. (1989). Our simulations show that this parameter has a profound impact on the erosion process as it influences both spatial distribution and magnitude of sediment flow and erosion/deposition rates. Recently, the importance of transport capacity for overland flow erosion processes is being fully recognized and more experimental and theoretical work is being performed (Guy et al. 1991, Govers 1991, Nearing et al. 1997). We further discuss this issue in a separate chapter. The following animation illustrates how the change in transportability changes the erosion regime from detachment capacity limited to transport capacity limited while also reducing the magnitude of erosion rates.

F igure 6. (animation) Impact of change in soil transportability: Kd=0.001, Kt=(0.0001,0.1)

It is important to note that the parameters do not act independently, they are interrelated and it is this interaction which controls the pattern and magnitude of erosion. For example, the growth of vegetation reduces both K d , K t and 1/n and the resulting erosion/deposition pattern depends on the interaction between the rates of this change. If both K d and K t change at the same rate, the spatial distribution of erosion/deposition stays the same and only the magnitude of rates changes. If vegetation growth reduces K d faster than K t , the erosion/deposition pattern will change from transport capacity limited towards detachment limited. The interrelation between the parameters is an open research question and there is a lack of systematic experimental and theoretical/modeling work in this area.

The presented analysis shows that for the uniform soil and cover there is a basic pattern of erosion and deposition in complex terrain which does not change significantly even if rainfall, soil and cover changes. High erosion risk areas are located on upper convex parts of hillslopes and in hollows and centers of valleys with concentrated flow. Deposition occurres in concave valleys and concave lower parts of hillslopes. With changing soil and cover conditions the line of starting deposition can move up- or down-slope, potentially influencing the sediment loads in streams. The analysis demonstrates that besides the obvious fact that the worst situation occurs for large events with high rainfall intensity when the soil surface is rather smooth (e.g. without a protective vegetation cover), sediment loads can be very high even for smaller events if soil is highly transportable. Change in the soil and cover from a uniform to spatially variable distribution has a great impact on the basic pattern of erosion/deposition (as we show later). Depending on the location this spatial variability can positively or negatively influence the overall soil loss as well as the sediment loads in streams.

2.2.2 Transport and detachment capacity equations

Our previous simulations (Mitas and Mitasova 1998) as well as several recent publications ( Govers 1991, Guy et al. 1991, Nearing et al. 1997) indicated that the transport capacity equation used in the WEPP model is probably not general enough for use in complex terrain conditions. Similar suggestions were made also about the detachment equation used in WEPP (Bjorneberg et al. 1997). For example, we were not able to reproduce completely the extent of areas with observed deposition. To address this issue we have attempted to study the sediment transport capacity relations as the key influencing quantity in our applications. We have tested both the power law shear stress relations (Julien and Simon 1985) with different power exponents and the new stream power based relation suggested by Nearing and co-workers (1997).

Julien and Simon (1985) analyzed the existing sediment transport equations and presented an equation in a general form:

q s = p q m sin ß n i d (1- t 0 / t) e (3)

where q s is the sediment flux, q is the water flux, ß is the slope angle, i is the rainfall intensity, t 0 , t are the critical shear stress and shear stress respectively, and p, m, n, d, e are experimental or physically based coefficients. The WEPP uses this equation with m=n=1.5 . We have tested it for values of m=0.6-2.0 . Willgoose (1989) has shown that the parameter m depends on the type of flow and channel geometry which indicates that in complex terrain and cover conditions this coefficient should be spatially variable depending on the type of flow in a particular area. Higher values of m lead to greater impact of water flow on erosion pattern, while for the lower values of m , terrain has more profound impact (see Figure 30 in applications).

For modeling erosion in a rill Nearing et al. (1997) presented an improved fit to several sets of experimental data by relating sediment load qs to stream power o :

with the constants A = -34.47, B = 38.61, C = 0.845, D=0.412 . Based on the conditions of experiments it was suggested that (4) could be a reasonable estimation of the sediment transport capacity. The equation (4) can be rewritten to the following form (Mitas and Mitasova 1997):

where a 0 =1380, b =88.90, d =0.179, o 0 =8.89.10 -6 , o is the stream power. This form of the equation (4) allows us to define a physical interpretation of the constants, as a 0 represents a saturated sediment load for infinitely large stream power, o 0 is a "reference" stream power, b=88.90 and d=0.179 are dimensionless exponents. Strictly speaking, the choice of the constants corresponds to the experimental results used in the fit and could be different in other cases, e.g., an effective transport capacity coefficient analogous to the one in (3) has to be included for different covers, etc. The important difference of the equations (3) and (4, 5) is that through the stream power the effect of flow velocity is directly incorporated into the transport capacity. For complex terrain and cover conditions, the flow velocity varies and can change dramatically with varying location, so we can expect differences in predicted patterns of erosion/deposition by using equation (5) as the sediment transport capacity.

Figure 7 . Comparison of different sediment transport capacity equations

We have applied the equations (3) and (5) to the experimental data from Germany for uniform soil and cover conditions with the following results. The areal extent of deposition predicted using the equation (3) with m=0.6 (37%) was closer to observations (colluvial deposits thicker than 10cm measured at about 40% of sampling sites of a regularly spaced grid), than the deposition predicted by the equation (3) with the m=1.5 , which covered significantly smaller area of about 24%.

The general pattern of erosion and deposition predicted by using the stream power based equation (5) for transport capacity was similar to the patterns obtained from shear stress based equation, there were significant quantitative differences as well as more dramatic spatial variability in the erosion regimes due to the variability of first order reaction coefficient. Our preliminary results indicate that the stream power based equation is significantly different from the usual sheer stress equation and deserves further investigation and experimental testing especially for different types of soils and larger sizes of experimental plots. When implementing a new transport capacity equation it is neccessary to investigate also the appropriateness of detachment capacity equation. More sophisticated estimation of detachment capacity was developed recently by (Nearing et al 1991) and used in a complex model of rill development.

2.2.3 Impact of spatially variable land cover and applications for land use management

In our previous report (Mitasova et al. 1996c) we have applied SIMWE to the experimental farm for the traditional land use management scenario (Figure 9A). This land use has resulted in severe erosion when the large storm event occurred during the time when the agricultural fields were bare. The simulations showed that the areas with the highest water and sediment flow potential were unprotected and significant rilling and gully formation occurred (Mitasova et al. 1996c). We have investigated the possibility to use the SIMWE model for analyzing and designing the placement of selected erosion protection measures based on land cover, as illustrated by the following simple example.

First, we used the model to identify locations with the highest erosion risk, assuming a uniform land use. Then, the protective grass cover was distributed to the high risk areas while preserving the extent of grass cover at the original 30% of the area (Figure 9C). We performed a simulation with the new land use to evaluate its effectiveness. The results demonstrate that the new design has a potential to dramatically reduce soil loss and sediment loads in the ephemeral streams (Figure 9C). The crest in sediment flow in the valley disappears and is replaced by light deposition within the grassways, while the maximum and total rates of erosion are significantly reduced (Mitas and Mitasova 1998). We have found, that the effectiveness of this design depends on differences in roughness, combination of very smooth bare soil and very dense grassway resulted in predictions of higher erosion along the borders of the grassway - more study is needed to decide whether this is an artifact of the method or a realistic prediction.

It is interesting to note, that the land use design obtained by this rather simple computational procedure, using only the elevation data, had several common features with the sustainable land use design proposed and implemented in 1993 at the farm, based on extensive experimental work (Auerswald et al., 1996). A simplified version of that landuse design, along with prediction of water and sediment flows and net erosion/deposition is presented in Figure 9B. It uses significantly higher proportion of permanent grass cover and fallow and increases roughness in the hop field. The results show that this design keeps lots of moisture and, at higher cost, reduces the erosion even further.

Figure 8 . Evaluation of 3 different land use alternatives : Simulated water depth, sediment flow rate and net erosion/deposition for the original (A 21% grass), new sustainable (B: 40% grass) and computer designed (C:19% grass) land use, for the time of the year when the agricultural land (brown) is bare and grass cover(green) is well developed .

2.2.4 Impact of temporal variability in land cover

The vegetation growth during the year changes the properties of soil surface including its roughness with profound impact on water and sediment flow and pattern of erosion and deposition. The same land use can lead to different erosion/deposition patterns and sediment loads depending on the time of year when the storm event occurs. The following image shows that the difference between the roughness of two different covers (scenario B, Figure 8) has a significant impact on the erosion/deposition pattern and areas which experienced erosion can become depositional areas during a different time of year.

Figure 9. Impact of difference in roughness for spatially variable cover (scenario B) where K t =K d =0.03/0.0003 for both cases and surface roughness increases in the area of bare soil while it remains constant for grass a) n=0.01/0.1 , b) n=0.05/0.1

This fact can be even better illustrated by the following example where we have simulated an impact of a) extremely large event ( 140mm/h ) and bare soil in agricultural fields ( n = 0.01, K t = 0.03, K d = 0.003, b) small event ( 18mm/h ) with dense cover ( n = 0.1, K t = K d = 0.0003 ). The simulation is in agreement with the hypothesis suggested by Auerswald (personal communication) that the material is deposited in the valley during repeated small events and then part of the accumulated material is eroded during extreme events with conditions leading to prevailing detachment limited case of erosion when large transport capacity of flow is present.

Figure 10. Sediment flow a), c) and net erosion/deposition b), d) for a large a),b), and a small c), d) event for scenario A

2.2.5 Simulation of impact of terrain structures

We have tested the capabilities of the SIMWE model to simulate water flow and erosion processes for terrain with structures such as terraces and ponds, when terrain is not smooth and significant discontinuities and depressions are present. We have modified the terrain surface using standard GIS tools ( r.digit, r.mapcalc, see the following chapter for more details ) resulting in a surface with a simplified pond (depression) and a terrace (discontinuity-fault and a small flat area with zero gradient). For the test runs we have used a hypothetical case of uniform soil and cover, even on structures. The SIMWE algorithm was robust enough to simulate the water and sediment flow even for this complex situation, as illustrated by Figure 11, which shows water and sediment accumulation within the pond and a terrace. If the terrace wall is not enforced, as in our example, it will be rapidly eroded away. The results are encouraging and allow us to target the simulation of more complex and realistic structures in future (see also the simulation of a road impact in the previous report by Mitasova et al. 1996c).

Figure 11 Impact of terrain structures on water flow and net erosion/deposition under uniform conditions

3. GIS tools

Simulation of landscape processes is more sensitive to noise and artifacts in landscape characterization data than the more traditional uses of GIS such as automatic map production or spatial analysis. Efficiency of simulations depends on digital data representation where the traditional representation by polygons, commonly used for mapping, is not always the best approach for simulations. Also, the models often include complex spatial and spatio-temporal relationships and their understanding requires more sophisticated graphical representations than the standard 2D maps. Therefore, there is a continuing effort to extend the GIS capabilities to support modeling and simulation of processes (both within commercial products and research tools) by implementing new, advanced methods and data structures. Our effort has focused on improving methods for multivariate spatial interpolation, topographic analysis and visualization and extending the data structures to support multivariate point and raster format. Some of these improved capabilities will be available within the new GRASS releases in cooperation with the Baylor University GRASS Research Group (http://www.baylor.edu/

Interpolation based on regularized spline with tension which has been enhanced and used for this project is described by Mitasova and Mitas (1993), Mitasova et al. (1995a,b), Mitasova et al. (1996). To gradually fulfill the requirements of spatial interpolation for GIS applications and simulations we are periodically enhancing the implementation of the RST method with the most recent enhancements including the support for spatially variable smoothing, which allows users to set a different smoothing parameter for each given point, depending on the accuracy of the measurement. This capability supports combination of data from various sources with different accuracies, with the resulting surfaces passing the closest to the most accurate data and allowing to deviate more from the data which are measured less accurately. We have also tested the interpolation tools in ArcView Spatial Analyst and found a serious flaw in the implementation and application of splines to a farm (crop yields, organic content, pH, etc.) used in the Spatial Analyst tutorial (ESRI 1997). We have provided the result of analysis to ESRI with suggestion to fix the flawed implementation of interpolation (Mitasova and Mitas 1997). The problem is being currently fixed.

Volume interpolation and visualization of soil data. To test the possibilities of creating 3D models of soil properties within a GIS we have computed a series of spatial models from comprehensive soil survey data for an experimental farm at Scheyern, Germany. Soil properties (data courtesy Dr. Auerswald), were measured in 3D space up to 1.2m depth and they included results of chemical analysis (pH, Nitrates, Phosphates, Potassium, Organic matter, etc.), soil texture analysis, and qualitative information for each sample. Using this data, it was possible to derive additional information and parameters such as the depth of colluvial deposits and hydraulic conductivity, needed for erosion simulations. From the point of view of spatial modeling a special challenge for representation and visualization of soil data poses the fact that the vertical spatial variability requires resolutions much higher than the resolutions used for the representation of phenomena in a horizontal plane. We have investigated various approaches to create a 3D model of the measured soil properties (Brown et al 1997), in particular by: a) sorting the data by horizons, interpolating a 2D raster map for each horizon at 2m resolution and creating a multiple surfaces model (Mitasova et al. 1996b) b) interpolating the 3D data to 3D raster map with 2m horizontal and 0.1m vertical resolution using the trivariate implementation of the RST method (Figure 12). To visualize vertical relationships with sufficient detail, we have used 100 times relative exaggeration of depths both for multiple surfaces representing the soil horizons (depths exaggerated relative to terrain surface) and for the series of volumetric data. The results are illustrated in this report by volume models of organic carbon, hydraulic conductivity and soil reaction (pH) combined with contours (Figure 12). We have found, that if the adequate tools are available, the full 3D model is more appropriate than the representation based on multiple surfaces as it incorporates the vertical relationships into interpolation and allows more efficient visual analysis. The 3D spatial model of organic carbon shows that its highest concentrations are in the area with long term grass cover. However, as expected, the amount of organic carbon rapidly decreases with depth (Figure 12a). The pH model shows greater spatial variability in vertical direction as the highest acidity on terrain surface, located in grass area, extends over larger area in deeper horizons (Figure 12c). The values of hydraulic conductivity were derived from information on particle size distribution for each sample. The interpolated 3D raster (Figure 12b) together with a DEM can be used as an input for 3D infiltration model, enhancing the realism of hydrologic simulations. Example of size fraction analysis visualization for points and a volume model of clay content is presented in Brown et al. (1997) including the results for nitrates, phosphates and other chemicals.

Figure 12. Soil chemical analysis and texture

Simplified structure design using standard GIS tools. Standard GIS tools, such as r.digit and r.mapcalc can be used to modify the terrain to include various man made structures, such as roads, ponds or terraces for the purpose of simulating impact of anthropogenic features on landscape scale erosion processes. The following procedure can be used to to add a simplified pond:

- display background info (terrain, water flow, rills, etc),
- use r.digit to outline the structure ( pond2.mask ) with depression as category 1, dam as category 2,
- use r.mapcalc to create depression and dam for a simplified pond with horizontal bottom:
pond2.el = if(pond2.mask,pel.2m)
pond2.dig = 1.5+pond2.el-461.3 (pond with min.depth 1.5m)
- run r.support to change no-data to 0 ( pond2.mask )
- use r.mapcalc to add the structure to terrain surface
pel.pond2a = pel.2m-pond2.dig


Similar simple procedure can be used to create a terrace. Both structures can be added to the terrain with the result illustrated in Figure 13. More complex structures are possible by developing the appropriate geometrical operations using r.mapcalc or by designing interactive surface design tools operating on raster data. Structures can be also imported from CAD tools or other software, transformed to raster representation and used to modify the terrain, however appropriate interface or import/export tools need to be developed.

Figure 13. Original terrain, mask locating the terrace and resulting terrain surface

4. Applications

Besides increasing our knowledge about erosion processes in landscapes the goal of this project is to develop methods and tools for erosion risk assessment and erosion prevention support for military installations. This task poses a special challenge, because military installations occupy large areas with terrains much more complex than typical agricultural regions for which most of the traditional erosion modeling tools were developed. Also the landuse at installations often combines relatively well preserved natural areas with landscape exposed to high intensity disturbance, well beyond the impact of agriculture. Therefore the principles of process based erosion modeling developed for agriculture have to be significantly enhanced and new approaches have to be designed to meet this challenge. We have already tested some of our approaches for areas at military installations (Yakima, Camp Shelby extension) as described by Mitasova et al. (1996bc, 1995b). In this report, we further demonstrate the developed methodology for military installations at Ft. Irwin and Ft. McCoy.

4.1 Ft. Irwin

To illustrate the issues associated with simulations for large areas we use an example of a mountainous region in California. The standard 30m DEM available for the entire study area (3000 square km) represents 4 million grid cells, a challenging data set for the current process-based simulation tools and workstations, at the resolution hardly sufficient for rough identification of high erosion risk areas. The DEM at 5m resolution, needed e.g. for capturing at least approximately the effects of roads or grassways, would require to use data sets with 121 million grid cells and run simulations which would be prohibitively expensive, if not impossible with the current computational resources. It is clear, that for such a large area, modeling at different scales and resolutions is needed, depending on the importance and complexity of area's watersheds. It is important to note that our aim in this application is to illustrate the possibilities to use standard elevation data for erosion simulations in a large area. We are not taking into account the climate, soil and cover properties which certainly have a profound impact on water flow in this area. In spite of the fact that this is only a hypothetical example, this test allows us to assess the capabilities of our tools and prepare the environment for more realistic applications (e.g. Hohenfels installation).

Digital elevation model and topographic analysis. To illustrate the issues of resolution, noise and systematic errors in standard elevation data we analyzed the 30m DEM available for Ft. Irwin (Figure 14) with more detailed analysis performed for the subareas A, B, C, D (Figure 15).

Figure 14. Fly-by over a DEM at Ft. Irwin

Figure 15. Topographic potential for soil detachment by water flow for entire Ft. Irwin with highlighted subareas used for further analysis.

GRASS GIS allows us to compute several important topographic parameters describing various geometrical properties of terrain, as illustrated for an area C for the original 30m DEM:

Figure 16. Topographic parameters

Topographic parameters serve as inputs for erosion models but they are also useful for evaluating the quality of a DEM and identifying possible noise and systematic errors as illustrated by the following example of terrain with draped tangential curvature. Tangential curvature for 30m DEM shows acceptable structure in mountainous area while significant noise and systematic errors (stripes) are present in lowland (Figure 17a). After smoothing and resampling to 10m resolution using the RST interpolation method the noise is reduced and the major topographic features become more visible (Figure 17b):

Figure 17 . Terrain with draped tangential curvature a) original 30m DEM and K_t , b) 10m DEM and K_t with smoothing 1.0, ten=60

These images clearly demonstrate that the need for precision and accuracy is spatially variable, with flatter areas much more sensitive than mountains. Note how the artificial structure in flat areas continuously transforms into the real terrain structure in mountains. This is especially "dangerous" phenomenon if a DEM is going to be used for simulations, as the artificial structure can be mistaken for the real topographic feature.

Flow tracing is an important part of hydrologic and erosion modeling. The results are significantly influenced by flow tracing algorithm and resolution as well as quality of the DEM as illustrated by the following example, which compares the steady state water flow maps based on the original 30m DEM and the smoothed and resampled DEMs (Figure 18).

Figure 18. Upslope contributing area computed from a) original 30m DEM for area B, b) 10m reinterpolated and smoothed DEM with smoothing 1., ten=60, area C.

Smoothing and resampling the original data with the RST reduced the artificial pits in the original DEM and allowed water flow to create continuous streams.

Topographic potential for erosion and deposition estimated by the USPED model. To illustrate the impact of smoothing and resampling on erosion modeling we have computed sediment transport capacity of water flow (Figure 19) and net erosion/deposition (Figure 20) at three resolutions: a) downscaled to 90m for the entire region, b) original 30m for a 500 square km subregion, c) smoothed and resampled to 10m for a 150 square km subregion. To demonstrate the differences in detail which can be achieved at various resolutions we have visualized the results as color maps draped over the 10m resolution DEM for a small subregion (36 square km). While the 10m resolution does not improve the accuracy of the original elevation model, the smoothing reduces the noise and high resolution allows better description of terrain geometry leading eventually to more realistic results of erosion model. The impact of noise and resolution is even more striking for modeling of net erosion/deposition (Figure 20) which is very sensitive to artifacts in a DEM as it is a function of second order derivatives (curvatures) of the elevation surface (Mitas and Mitasova 1997).

Figure 19. Topographic potential for soil detachment computed at different resolutions a) 90m, area A, b) 90m zoomed into area D, c) original 30m DEM, area B, d) 30m area D, e) reinterpolated and smoothed at 10m, area C, f) 10m area D.

Figure 20. Topographic potential for net erosion and deposition computed at different resolutions a) 90m, area A, b) 90m zoomed into area D, c) original 30m DEM, area B, d) 30m area D, e) reinterpolated and smoothed at 10m, area C, f) 10m area D.

Water and sediment flow simulation by SIMWE. To test the applicability of SIMWE to areas significantly larger than the test Germany data we have computed the spatial distribution of steady state water flow, sediment flow and net erosion/deposition for a 36 square km subregion D (Figures 21, 23). This application demonstrates that processing of original elevation data by adequate tools, such as in our case the RST method and application of a robust, process-based model can lead to a significant improvement in predicted erosion/deposition pattern.

It is also possible to simulate approximately the dynamics of water flow during a storm - the animation shows the development of water depth during and after an uniform stationary storm which lasted approx. 5hrs (Figure 22).

Figure 22. Dynamic model of water flow depth during and after a uniform stationary storm.

Sediment flow rates are estimated by solution of continuity of mass equation using SIMWE showing high sediment flow rates in centers of valleys with dispersal of sediment flow in some areas (Figure 23a). Net erosion/deposition rates are estimated for a case with prevailing transport capacity limiting regime (Figure 23b). Note the disappearing and/or split gullies as the terrain flattens and alluvial cones are formed.

Figure 23. Spatial distribution of a) sediment flow b)net erosion/deposition rates for uniform soil and cover

The resampled and smoothed DEM and the results of simulation by the SIMWE model were provided for a development and demonstration of virtual reality tools for hydrologic simulations for CAVE at NCSA (Johnston and Reez, 1997) as illustrated by Figure 24. VRTools.

4.2 Ft. McCoy

We illustrate the estimation of topographic potential for erosion and deposition based on the USPED model using the elevation data provided at 30m horizontal resolution and 1m vertical resolution , for the following subarea of Ft. McCoy (Figure 25).

Figure 25. DEM for Ft.McCoy with highlighted test area.

Topographic potential for soil detachment and for net erosion and deposition is estimated by the USPED model (Mitasova et al. 1996). Even with rather crude elevation data, it is possible to identify the areas with high topographic risk for soil detachment and erosion/deposition, as illustrated by the following sequence of maps showing the input 30m DEM (Figure 26a), intermediate steps (slope, upslope contributing area, Figures 26b, 26c) and results of computations (Figures 27a, 27b). Note the impact of plateaus in low elevation areas on the slope pattern (artificial steeper slopes along 1m contours), highlighted by red arrows. These plateaus are due to the insufficient vertical resolution of 1m. Upslope contributing area as a measure of steady state water flow computed from the original 30m DEM (Figure 26c) by r.flow is strongly underestimated. Pits and plateaus in the DEM cause problems in flowtracing with almost no flow generated in lower elevation areas and disrupted flow in valleys.

F igure 26 . Inputs and intermediate results for computation of erosion/deposition by USPED using original 30m DEM.

Figure 27. Topographic potential for soil detachment and net erosion/deposition computed by USPED using the original 30m DEM.

Modified LS factor predicts well some areas of high potential for soil detachment, especially in hilly parts of the region. However, it does not predict the location of areas with concentrated flow with high potential for rilling and gully formation . Also, in lower elevation areas the artificial pattern of increased erosion is predicted along 1m contours. The net erosion/deposition map computed by the USPED model predicts high net erosion in hilly regions and on slopes along the streams. It also shows that a significant portion of the material eroded from hillslopes is deposited before it could reach the main streams. However, this map lacks the prediction of high erosion due to concentrated flow in valleys and shows artificial waves of erosion and deposition along the 1m contours in flatter areas.

To reduce the negative impact of lower resolution of the available elevation data the given DEM was reinterpolated to 10m horizontal and 0.01m vertical resolution using the RST method. Simultaneously with interpolation, maps of slope, aspect, profile and tangential curvatures were generated (Figure 28).

F igure 28. DEM reinterpolated to 10m resolution with simultaneous topographic analysis a) elevations, b) slope, c) aspect, d) profile curvature, e) tangential curvature.

Note, that in the slope map the artificial pattern of steeper slopes along 1m contours disappeared.

Figure 29. Steady state water flow.

Steady state water flow computed by r.flow from the smoothed 10m DEM shows potential for channel formation in valleys and predicts water flow also in low elevation region. Flow in the two main streams is not adequately described because of lack of data, but it can be incorporated if stream data are available.

Figure 30. Modified LS factor or distribution of topographic potential for soil detachment by overland flow.

Modified LS factors were computed from the 10m DEM using different exponents for the water flow term ( p=0.6 and p=1.5 ). Value of this exponent is still a subject of research and discussion in erosion research community. The first result puts more weight on the influence of slope while the second result puts more weight on the influence of water flow. Because the exponent depends on the conditions of water flow in a particular area it should be calibrated to reflect the type of flow typical for the modeled area and time during the year.

Figure 31. Topographic potential for net erosion/deposition computed by USPED

Topographic potential for net erosion/deposition was computed from the 10m DEM using the USPED model. Similarly as for the result of 30m DEM, the model shows high erosion in hilly area and along main streams and deposition in concave areas. It also indicates high erosion in areas with concentrated flow which could reach the main streams. The artificial pattern of erosion/deposition along contours is not present.

Impact of soil and cover will be incorporated when the land use/cover/soil categories or at least a rough estimate of C-factor are available. Also, estimation of sediment flow and net erosion/ deposition by process based distributed model SIMWE can be performed after we get data on land cover.

5. Conclusions and future directions

The presented approach and examples illustrate several aspects of advanced GIS application to landscape characterization and simulations of processes. We have demonstrated the importance of a proper choice of interpolation method when preparing the input data for simulations and the usefulness of smoothing and resampling of a standard 30m DEM to higher resolution for modeling of erosion and deposition. Replacing the geometry-based algorithms by physics-based models improves the flow related topographic analysis. Extension of GIS to 3D allows us to create spatial models which capture the distribution of phenomena in 3D space, however, anisotropic scaling and resolution is needed to create meaningful models. Implementation of concept of multivariate fields for landscape characterization data in GIS and development of appropriate supporting tools increases the efficiency of data preparation and of analysis and presentation of simulation results. This approach further supports the move from profile and/or polygon-based models, to full 3D dynamic simulations based on multivariate fields.

The stochastic method of solving the first principles equations using Green's function Monte Carlo technique provided us with a valuable research tool of a much needed robustness and flexibility. It enabled us to investigate several important issues such as erosion/deposition regimes, forms of sediment transport capacity and different landuse scenarios in a complex realistic landscape. Using the bivariate formulation, we have theoretically elucidated the observed relation between the pattern of erosion/deposition and the terrain shape in the transport capacity limited regime. In particular, we have shown the relation of terrain profile and tangential curvatures to the observed patterns and we have demonstrated that the impact of both curvatures is equally important for a proper description and understanding of erosion in 3D space.

A suggestion of Nearing at al (1997) that the sediment loads from rills may be more strongly influenced by a sediment transport limit rather than by the soil detachment, in general, agrees with our results. This seems to be true especially in complex terrain conditions where transport capacity changes significantly due to variations in terrain shape and cover affecting significantly the distribution and amplitudes of the water flow field. Our calculations and analysis also suggest that the sediment transport capacity plays a more important role than anticipated by the previous research which focused on erodibility as the key control quantity. Obviously, a subtle and spatially variable interplay between erodibility and transport capacity can influence the processes in a profound way. We believe that this complexity clearly points out towards the importance of the high accuracy-high resolution 2D simulations as one of the most promising research areas.

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Problem with interpolating a vertical profile in 3D Analyst (ArcMap 10.1) - Geographic Information Systems

1 Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka, Japan

2 Environmental GIS Laboratory Co. Ltd, Fukuoka, Japan

Copyright © 2019 by author(s) and Scientific Research Publishing Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

Received: September 11, 2019 Accepted: October 27, 2019 Published: October 30, 2019

In this study, we developed a new computational fluid dynamics (CFD) model called Airflow Analyst that deepens the affinity between CFD and geographic information system (GIS). First, a precise simulation of the surface-mounted cube was conducted. Validation testing based on the obtained data confirmed the predictive accuracy of Airflow Analyst. Second, New National Stadium Japan (Tokyo Olympic Stadium) was accurately reproduced in a computer, capturing the latest detailed urban area data for the base. For the target of the constructed 3D models, simulations with a large number of grid points/cells (CFD) were conducted. These simulations reproduced the complex turbulent flow fields both inside and outside the stadium. The experiment successfully reproduced the CFD simulation using a large number of grid points/cells, where the conditions of the wind flow ventilation from the sky were similar to those of the intended stadium design.

CFD, LES, GIS, Surface-Mounted Cube, New National Stadium Japan

Urban environments in our society are affected by the various impacts of wind, such as strong wind damage and ventilation. Thus, the built environment in urban design and regional planning needs to be assessed and optimized. Every step during the examination process for plans requires the involvement of citizens, administrators, and developers. Our research team was engaged in software development for urban planning and architecture design, focusing on new computational fluid dynamics (CFD) [1] with a high affinity for geographic information system (GIS) [2]. Recent research results in the fields we are interested in have been reviewed in the literature [1]. We have been developing Airflow Analyst [3] [4] [5] [6], which is extension software for ArcGIS, which is versatile GIS software. The biggest advantage of Airflow Analyst is that users are able to analyze the flow field characteristics or passive scalar transport and diffusion in regions with arbitrarily shaped objects without considering landform features or building clusters.

Airflow Analyst, running on the GIS system, has the following advantages:

1) Large labor savings in the preparation of three-dimensional (3D) data for urban areas due to its usage of geospatial information resources, which are circulated free-of-charge or sold

2) Large-scale reductions in constructing 3D data planning due to the integration of computer-aided design (CAD) and building information modeling (BIM)

3) Instant simulation with intuitive operation due to the visual confirmation of scales on maps for computational wind directions, domains, and grid generation

4) Visualizing the computational results of 3D data on a map and storing geographic information-spatial references by coordinates, thereby enabling superpositions of spatial analysis with other spatial information and

5) The availability to distribute and share using superpositions with web mapping services.

Conventional wind-flow simulations for urban areas require specialized knowledge and skills, as well as enormous amounts of time and labor to create 3D models and generate grids integrating local terrain], building shapes, and design plans. To reflect wind-flow simulations in a plan or a design, however, a system with simple and intuitive operation is required to examine data immediately after being obtained. This system should reduce the work involved in the above CFD analysis, and planners should be able to appropriately perform numerical simulations.

Governments and open data communities, both inside and outside Japan, are rapidly and increasingly providing free 3D data management. Therefore, geospatial information infrastructures have been established with 3D urban data. Specifically, BIM and Construction Information Modeling (CIM) are employed, with enhanced data compatibility with GIS. BIM and CIM have also enabled 3D data usage in design planning. Along with this, advanced techniques have been developed to extract data from satellite and aerial photographs, as well as photogrammetric image data acquired by unmanned aerial vehicles (UAVs). A new data creation method has been proposed: point cloud data obtained via laser surveying can generate minute 3D data, and all these data can be analyzed via integration with the GIS.

Airflow Analyst is the first software program in the world to complete the process from grid generation to the visualization of data computation results. The present report demonstrates the first validation test of the predictive accuracy of Airflow Analyst for a surface-mounted cube. Second, this paper introduces a reproduction of the New National Stadium Japan (Tokyo Olympic Stadium) using 3D topography Advanced World 3D Map (AW3D) data [7], which were created from a combination of high-resolution images taken by satellite photographs. Then, we report large-scale numerical simulation cases for wind flow. Other analyses developed using Airflow Analyst are described in the Appendix.

2. Summary of the Airflow Analyst Software

For the numerical simulations, we used the Airflow Analyst software package, for which a collocated grid in a general curvilinear coordinate system was adopted. In this collocated grid, the velocity components and pressure are defined at the grid cell centers, and variables that result from multiplying the contravariant velocity components by the Jacobian are defined at the cell faces. For the numerical technique, we adopted the finite difference method (FDM), and a large-eddy simulation (LES) model was used for the turbulence model. In the LES model, a spatial filter was applied to the flow field to separate eddies of various scales into the grid-scale (GS) components, which are larger than the computational grid cells, and sub-grid scale (SGS) components, which are smaller than the computational grid cells. Large-scale eddies, i.e., the GS components of turbulence eddies, are directly numerically simulated without the use of a physically simplified model. In contrast, dissipation of energy, which is the main effect of small-scale eddies, i.e., the SGS components, is modeled according to a physics-based analysis of the SGS stress.

For the equations governing the flow, a filtered continuity equation for incompressible fluid (Equation (1)) and a filtered Navier-Stokes equation (Equation (2)) are used. When passive scalar transport and diffusion were considered, the standard convection-diffusion equation for a passive scalar was solved with the linkage of the above equations of flow field characteristics.

Since we investigated wind fields with mean wind speeds of 5 - 10 m/s, the effects of the vertical thermal stratification of the atmosphere (atmospheric stability) were negligible. For the computational algorithm, a method similar to a fractional step (FS) method [8] was used, and a time marching method based on the Euler explicit method was adopted. Poisson’s equation for pressure was solved using the successive over-relaxation (SOR) method. To discretize all the spatial terms except for the convective term in Equation (2), a second-order central difference scheme was applied. For the convective term, a third-order upwind difference scheme was applied. The interpolation technique by Kajishima [9] was used for the fourth-order central differencing that appears in the discretized form of the convective term. For the weighting of the numerical diffusion term in the convective term discretized by third-order upwind differencing, α = 0.5 was used instead of α = 3.0 from the Kawamura-Kuwahara scheme [10] to minimize the influence of numerical diffusion. For LES SGS modeling, the standard Smagorinsky model [11] was adopted with a model coefficient of 0.1, in conjunction with a wall-damping function (Equations (3)-Equation (8)).

∂ u ¯ i ∂ t + u ¯ j ∂ u ¯ i ∂ x j = − ∂ p ¯ ∂ x i + 1 R e ∂ 2 u ¯ i ∂ x j ∂ x j − ∂ τ i j ∂ x j (2)

τ i j ≈ u ′ i u ′ j ¯ ≈ 1 3 u ′ k u ′ k ¯ δ i j − 2 ν S G S S ¯ i j (3)

ν S G S = ( C s f s Δ ) 2 | S ¯ | (4)

| S ¯ | = ( 2 S ¯ i j S ¯ i j ) 1 / 2 (5)

S ¯ i j = 1 2 ( ∂ u ¯ i ∂ x j + ∂ u ¯ j ∂ x i ) (6)

3. Validation Testing of the Prediction Accuracy of the Airflow Analyst Software

Here, the results of validation testing are reported for the prediction accuracy of the airflow with a surface-mounted cube. Qualitative and quantitative assessments were conducted using flow visualization and turbulent flow measurement with a split-film anemometer. Figure 1 shows a bird’s eye view of the thermally stratified wind tunnel used for the present study. Figure 2 shows a side view of the surface-mounted cube’s setting state. Figure 3 depicts a close-up view of the surface-mounted cube in the wind tunnel’s test section.

As shown in Figure 4, the U-shaped horseshoe vortex surrounding a surface-mounted cube is apparent [12]. In the present simulation, a horseshoe vortex was reproduced with a 3D structure, as shown in Figure 5. Figure 5 visualizes the flow by using the smoke-wire technique in the wind tunnel experiment, which used the flow field in the vicinity of the surface-mounted cube. We observed that a highly complex flow field formed in the vicinity of the surface-mounted cube. Figure 6 provides the results of the simulations. Comparing both images shown in Figure 6 and Figure 8, Figure 6 demonstrates reproductions of flow patterns similar to the wind tunnel experimental result in Figure 7. Figure 8 shows the streamline image for the time-average flow field of the simulations. Observing this result, a standing vortex formed behind the surface-mounted cube in the time-average flow field. At the measurement station set in the center of the vortex (indicated by the black line in the figure), we focus on a graph comparing the vertical profile of U-velocity obtained from the


Conclusions

The vascular plant richness of the Etna Mountains discussed in this paper highlighted the floristic value of this volcano and its unique role in the context of Sicily. This contribution wanted to go one further step by using quantitative data on the spatial distribution of the plant species along the altitudinal gradient and highlight areas with a high concentration of narrow endemic species. In this way, it has been possible to highlight the remarkable altitudinal variations in the species distribution, which is less obvious than one might think in particular, the peculiar ”hump-shaped pettern” of floristic richness at medium altitudes was highlighted corresponding where both thermophilic and cooler species can find favorable environmental conditions. In relation to the area of each altitudinal belt, the total endemic and narrow species show an increasing response correlated with the elevation. The high degree of endemic species on the highest peaks of Mt. Etna is linked to its geographical, geological and climatic isolation, all important drivers of speciation acting on the population gene flows. The endemism despite having such peculiar and exclusive species to Etna, on the whole shows lower numerical values than other nearby floristic districts that are not volcanic but comparable in size and altitude. A plausible explanation is that the geological substrate youth and the frequent destruction of the plant communities following the lava flows have contributed to selecting a restricted number of species well adapted to these peculiar conditions. Etna as a whole remains unique in the Mediterranean and for this reason it has not been possible to make comparisons with the flora of the active volcanoes of this region, all considerably smaller and of much lower altitude.


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