1. GIS in Water Resources Review for Midterm Exam

2. Data Models A geographic data model is a structure for organizing geospatial data so that it can be easily stored and retrieved. Geographic coordinates Tabular attributes

3. Raster and Vector Data Point Line Polygon VectorRaster Raster data are described by a cell grid, one value per cell Zone of cells

4. ArcGIS Geodatabase Geodatabase Feature Dataset Feature Class Geometric Network Object Class Relationship Workspace

5. Geodatabase and Feature Dataset zA geodatabase is a relational database that stores geographic information. zA feature dataset is a collection of feature classes that share the same spatial reference frame.

6. Feature Class A feature class is a collection of geographic objects in tabular format that have the same behavior and the same attributes. Feature Class = Object class + spatial coordinates

7. Object Class An object class is a collection of objects in tabular format that have the same behavior and the same attributes. An object class is a table that has a unique identifier (ObjectID) for each record

8. Relationship Relationship between spatial and non-spatial objects Water quality data (non-spatial) Measurement station (spatial)

9. National Hydro Data Programs National Elevation Dataset (NED) National Hydrography Dataset (NHD) Watershed Boundary DatasetNED-Hydrology

10. http://www.ftw.nrcs.usda.gov/stat_data.html 1:250,000 Scale Soil Information

11. National Land Cover Dataset http://landcover.usgs.gov/nationallandcover.html http://seamless.usgs.gov/Get the data:

12. National Water Information System Web access to USGS water resources data in real time http://waterdata.usgs.gov/usa/nwis/

13. Flow Time Time Series HydrographyHydro Network Channel System Drainage System Arc Hydro Components GIS provides for synthesis of geospatial data with different formats

14. Geodesy, Map Projections and Coordinate Systems Geodesy - the shape of the earth and definition of earth datums Map Projection - the transformation of a curved earth to a flat map Coordinate systems - (x,y) coordinate systems for map data

15. Latitude and Longitude in North America 90 W 120 W 60 W 30 N 0 N 60 N Austin: (30°N, 98°W) Logan: (42°N, 112°W)

16. Length on Meridians and Parallels 0 N 30 N  ReRe ReRe R R A B C  (Lat, Long) = ( , ) Length on a Meridian: AB = R e  (same for all latitudes) Length on a Parallel: CD = R  R e  Cos  (varies with latitude) D

17. Example 1: What is the length of a 1º increment along on a meridian and on a parallel at 30N, 90W? Radius of the earth = 6370 km. Solution: A 1º angle has first to be converted to radians  radians = 180 º, so 1º =  /180 = 3.1416/180 = 0.0175 radians For the meridian,  L = R e  km For the parallel,  L = R e  Cos   Cos   km Parallels converge as poles are approached

18. Example 2: What is the size of a 1 arc-second DEM cell when projected to (x,y) coordinates at 30º N? Radius of the earth = 6370 km = 6,370,000m = 6.37 x 10 6 m Solution: A 1” angle has first to be converted to radians  radians = 180 º, so 1” = 1/3600 º = (1/3600)  /180 radians = 4.848 x 10 -6 radians For the left and right sides,  L = R e  6.37 x 10 6 * 4.848 x 10 -6 =  30.88m For the top and bottom sides,  L = R e  Cos  = 6.37 x 10 6 * 4.848 x 10 -6 * Cos 30º = 30.88 x 0.8660 = 26.75m Left and right sides of cell converge as poles are approached

19. Horizontal Earth Datums An earth datum is defined by an ellipse and an axis of rotation NAD27 (North American Datum of 1927) uses the Clarke (1866) ellipsoid on a non geocentric axis of rotation NAD83 (NAD,1983) uses the GRS80 ellipsoid on a geocentric axis of rotation WGS84 (World Geodetic System of 1984) uses GRS80, almost the same as NAD83

20. Vertical Earth Datums A vertical datum defines elevation, z NGVD29 (National Geodetic Vertical Datum of 1929) NAVD88 (North American Vertical Datum of 1988) takes into account a map of gravity anomalies between the ellipsoid and the geoid

21. Coordinate System (  o, o ) (x o,y o ) X Y Origin A planar coordinate system is defined by a pair of orthogonal (x,y) axes drawn through an origin

22. Universal Transverse Mercator Uses the Transverse Mercator projection Each zone has a Central Meridian ( o ), zones are 6° wide, and go from pole to pole 60 zones cover the earth from East to West Reference Latitude (  o ), is the equator (Xshift, Yshift) = (x o,y o ) = (500000, 0) in the Northern Hemisphere, units are meters

23. UTM Zone 14 Equator -120° -90 ° -60 ° -102°-96° -99° Origin 6°

24. ArcInfo 9 Spatial Reference Frames Defined for a feature dataset in ArcCatalog Coordinate System –Projected –Geographic X/Y Domain Z Domain M Domain

25. X/Y Domain (Min X, Min Y) (Max X, Max Y) Maximum resolution of a point = Map Units / Precision e.g. map units = meters, precision = 1000, then maximum resolution = 1 meter/1000 = 1 mm on the ground Long integer max value of 2 31 = 2,147,483,645

26. Four Points

27. One degree box and its four lines Geographic Coordinates

28. One Degree Box in USGS Albers Projection

29. USGS Albers Projection

30. Area Calculation in USGS Albers Area = 9130.6 km 2 111.79 km 82.26 km 81.09 km 82.26 + 81.09 2 x 111.79 = 9130.5 km 2

31. North American Albers Projection Same projection method as USGS Albers but different parameters

32. Area Calculation in North American Albers Area = 9130.6 km 2 118.17 km 77.89 km 76.64 km 77.89 + 76.64 2 X 118.17 = 9130.4 Take home message: Lengths of lines change but area is constant in Albers

33. x y f(x,y) Two fundamental ways of representing geography are discrete objects and fields. The discrete object view represents the real world as objects with well defined boundaries in empty space. The field view represents the real world as a finite number of variables, each one defined at each possible position. (x 1,y 1 ) PointsLinesPolygons Continuous surface

34. Vector and Raster Representation of Spatial Fields VectorRaster

35. Numerical representation of a spatial surface (field) Grid TIN Contour and flowline

36. Grid Datasets Cellular-based data structure composed of square cells of equal size arranged in rows and columns. The grid cell size and extent (number of rows and columns), as well as the value at each cell have to be stored as part of the grid definition. Number of columns Number of rows Cell size

37. Raster Sampling from Michael F. Goodchild. (1997) Rasters, NCGIA Core Curriculum in GIScience, http://www.ncgia.ucsb.edu/giscc/units/u055/u055.html, posted October 23, 1997

38. From: Blöschl, G., (1996), Scale and Scaling in Hydrology, Habilitationsschrift, Weiner Mitteilungen Wasser Abwasser Gewasser, Wien, 346 p. Extent Spacing The scale triplet Support

39. Spatial Generalization Central point ruleLargest share rule

40. Raster calculation – some subtleties Analysis extent + = Analysis cell size Analysis mask Resampling or interpolation (and reprojection) of inputs to target extent, cell size, and projection within region defined by analysis mask

41. Interpolation Estimate values between known values. A set of spatial analyst functions that predict values for a surface from a limited number of sample points creating a continuous raster. Apparent improvement in resolution may not be justified

42. Topographic Slope Defined or represented by one of the following –Surface derivative  z –Vector with x and y components –Vector with magnitude (slope) and direction (aspect)

43. Hydrologic processes are different on hillslopes and in channels. It is important to recognize this and account for this in models. Drainage area can be concentrated or dispersed (specific catchment area) representing concentrated or dispersed flow.

44. Drainage Density D d = L/A EPA Reach Files100 grid cell threshold1000 grid cell threshold

45. Network Definition A network is a set of edges and junctions that are topologically connected to each other.

46. Edges and Junctions Simple feature classes: points and lines Network feature classes: junctions and edges Edges can be –Simple: one attribute record for a single edge –Complex: one attribute record for several edges in a linear sequence A single edge cannot be branched No!!

47. Polylines and Edges

48. Junctions Junctions exist at all points where edges join –If necessary they are added during network building (generic junctions) Junctions can be placed on the interior of an edge e.g. stream gage Any number of point feature classes can be built into junctions on a single network

49. Connectivity Table J124 J125 J123 J126 E1 E3 E2 J123J124, E1 J124J123, E1J125, E2J126, E3 J125J124, E2 J126J124, E3 JunctionAdjacent Junction and Edge This is the “Logical Network” p. 132 of Modeling our World

50. Flow to a sink

51. Network Tracing on the Guadalupe Basin

52. Linear Referencing Where are we on a line?

53. Addressing

54. Arc Hydro Framework with Time Series Spatial relationship classes Temporal classes and relationships Geometric network

55. Space-Time Cube TSDateTime TSTypeID TSValue FeatureID Time Space Variable Data Value

56. MonitoringPointHasTimeSeries Relationship

57. TSTypeHasTimeSeries

58. Arc Hydro TSType Table Type Index Variable Name Type Of Time Series Info Regular or Irregular Units of measure Time interval Recorded or Generated Arc Hydro has 6 Time Series DataTypes 1.Instantaneous 2.Cumulative 3.Incremental 4.Average 5.Maximum 6.Minimum

59. Tracking Analyst Display

60. DEM Based Watershed and Stream Network Delineation Steps DEM Reconditioning/Burning in Streams Fill Sinks Eight direction pour point model to evaluate flow directions Flow accumulation Threshold stream network definition Stream segmentation Watershed delineation Raster to vector conversion of streams and watersheds

61. + =  Take a mapped stream network and a DEM  Make a grid of the streams  Raise the off-stream DEM cells by an arbitrary elevation increment  Produces "burned in" DEM streams = mapped streams “Burning In” the Streams Synthesis of Raster and Vector data

62. AGREE Elevation Grid Modification Methodology

63. Filling in the Pits DEM creation results in artificial pits in the landscape A pit is a set of one or more cells which has no downstream cells around it Unless these pits are filled they become sinks and isolate portions of the watershed Pit filling is first thing done with a DEM

64. 675649 524837 585522 30 675649 524837 585522 30 Slope: Hydrologic Slope - Direction of Steepest Descent

65. 32 16 8 64 4 128 1 2 Eight Direction Pour Point Model Water flows in the direction of steepest descent

66. Flow Direction Grid 32 16 8 64 4 128 1 2

67. Cell to Cell Grid Network Through the Landscape Stream cell

68. Contributing Area Grid 11111 1 1 1 1 1 1 1 1 143 3 12 2 2 3 2 16 6 25 11 1 11 1 1 1 1 1 1 1 1 1 433 12 2 2 2 3 16 256 Drainage area threshold > 5 Cells

69. Delineation of Streams and Watersheds on a DEM

70. Watershed and Drainage Paths Delineated from 30m DEM Automated method is more consistent than hand delineation

71. 5 5 1 1 1 3 2 2 3 3 4 4 4 4 4 5 5 6 6 6 Stream Segments in a Cell Network

72. Same Cell Value Subwatersheds for Stream Segments

73. Vectorized Streams Linked Using Grid Code to Cell Equivalents Vector Streams Grid Streams

74. For every stream segment, there is a corresponding catchment Catchments are a tessellation of the landscape through a set of physical rules Delineated Catchments and Stream Networks

75. Raster Zones and Vector Polygons Catchment GridID Vector Polygons DEM GridCode Raster Zones 3 4 5 One to one connection

76. Watershed A watershed is the area draining to any point on the stream network A new kind of connectivity: Area flows to a point on a line

77. Connecting Drainage Areas to the Network Area goes to point on line

78. HydroID – a unique identifier of all Arc Hydro features HydroIDs of Drainage PointsHydroIDs of Catchments