2. The Joy of GRID: Geomorphology and Hydrology in GIS Finn Krogstad UW Forest Engineering http://students.washington.edu

3. Consider Sediment Routing x 0,z 0 zz x x 4,z 4 x 3,z 3 x 2,z 2 x 1,z 1

4. Times Change Spatial problems used to require lots of programming. With modern spreadsheets, we could assign it as an undergraduate homework problem. GRID offers the same spreadsheet simplicity and functionality, but handles spatial issues for you.

5. OUTLINE A. GRID BASICS 1. GIS Data 2. Thinking in GRID 3. Programming B. HYDROLOGIC PROCESSES 1. Local 2. Watershed C. ANALYSIS 1. Classification 2. Regression

6. GRID BASICS - GIS Data

7. Points

8. GRID BASICS - GIS Data Points Arcs Polygons

9. GRID BASICS - GIS Data Points Arcs Polygons Attribute Tables

10. GRID BASICS - GIS Data Points Arcs Polygons Attribute Tables Data Sources

11. GRID BASICS - Thinking in GRID GRID-ing the World continuous discrete

12. GRID BASICS - Thinking in GRID GRID-ing the World Grid Algebra

14. GRID BASICS - Thinking in GRID GRID-ing the World Grid Algebra Spatial Spreadsheet - not mysterious - intuitiveness - flexible

15. GRID BASICS - Programming Command Line –just like you type it Flow Control –if, do, while User Interface –for GIS novices, e.g. SEDMODL

16. Hydrologic Processes Local –Slope, Aspect, Curvature Z = Ax 2 y 2 + Bx 2 y + Cxy 2 + Dx 2 + Ey 2 + Fxy + Gx + Hy + I

17. Hydrologic Processes Local –Slope, Aspect, Curvature –Hillshade Display Topography Radiant Energy Other things

18. Hydrologic Processes Local –Slope, Aspect, Curvature –Hillshade Watershed

19. Hydrologic Processes Local Watershed –Flow direction Lowest Neighbor Gradient

20. Hydrologic Processes Local Watershed –Flow direction –Flow accumulation Upslope Area Streams Watersheds Variable Inputs Cumulative Impact

21. Hydrologic Processes Local Watershed –Flow direction –Flow accumulation –Flow length distance to stream transport ‘friction’ delivery to streams

22. Multivariate Analysis

23. ClusteringClustering ‘True’ color Bands 1,4,7

24. Scatter Plots ClusteringClustering image Scatter-plots

25. Cluster Training ClusteringClustering Image Stand cover

26. Cluster Training ClusteringClustering Image Stream cover

27. Cluster Training ClusteringClustering Image Water bodies

28. Image Classification Image Classification

29. Classification vs. End Member Classification - We can classify a cell according to which class gives a higher likelihood. End Member - The fraction of each end member can be approximated by saving the normalized likelihoods.

30. Multivariate Analysis Clustering Regression Linear Ey = a 0 + a 1 x 1 + a 2 x 2 + a 3 x 3 +.... E(precip) = a 0 + a 1 longitude + a 2 elevation

31. Multivariate Analysis Clustering Regression Linear Ey = a 0 + a 1 x 1 + a 2 x 2 + a 3 x 3 +.… Logistic Ey=1/(1+(exp(-(a 0 +a l x l +a 2 x 2 +a 3 x 3 +...))) L-0.0018 M-0.0026 H-0.0037 Landslide Probability E(LS)=1/(1+(exp(-(a 0 +a l SMORPH)))

32. Conclusions GRID should be used like Excel Get yourself a wonk Keep up on data sources Use models to predict results Use observations to improve models

33. Instructors Finn Krogstad Peter Schiess

34. Schedule Lecture: Tuesday, 9:30-11:20, in BLD 261 Lab: Thursday, 9:30-11:20, in BLD 261 move?

35. Readings Cell-based Modeling with Grid Assigned readings to follow

36. Grading FE423: 50% labs, 50% exam FE523: 33% labs, 33% exam, 33% project

37. Final Exam 10:30-12:20 p.m. Wednesday, Mar. 15, 2000 open books, open notes, pencil-and-paper solution/discussion of several problems.

38. Labs Post lab reports on their web site Grading will be based on communication Finished and posted one week after assigned. Late work will be accepted with half the points deduced for each week it is late. Revise and resubmit each lab.

39. FE523 Project A course related project of your choosing. 1/20 Proposal 2/24 Progress Report 3/15 Final Report