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Automated Solutions to Water Resource Evaluations
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  1. Automated Solutions to Water Resource Evaluations Katherine Skalak, EIT ODNR Floodplain Management Program Melissa Williams, PE, GISP Stantec Consulting Ryan Branch Stantec Consulting 2012 Ohio GIS Conference September 19 - 21, 2012 | Hyatt Regency Hotel | Columbus, Ohio

  2. Agenda • CNMS Background and Overview • Ohio CNMS Stats • Data Model • Automated Solutions

  3. What is CNMS? • Coordinated Needs Management Strategy • Geospatial inventory of FEMA studies and mapping needs • “Living” Database • Continuous new input and assessment • “Valid” Streams reassessed every five years • Tracks needs, requests, and study status • Risk MAP – Mapping Assessment and Planning • Critical component for multi-year planning • National Level Reporting Tool

  4. CNMS Objectives and Overview • CNMS allows for: • Nationally consistent practice • Means for recording the voice of communities • Complete visibility • Record of the inventory • Status of the inventory • Means for measuring progress (metric) toward an operational goal – accountability • Means for tracking current activities • Means for projecting progress and planning for success

  5. CNMS Data Process

  6. Simplified CNMS Lifecycle Diagram Input CNMS Phase 3 Mapped Inventory Stream Reassessed in 5 years Study Assessed: Stream Valid? YES NO Input Unmapped Requests Restudy makes stream Valid Stream Studied

  7. CNMS Inventory (S_Studies_Ln) • Flooding source centerlines • FEMA’s FIRM inventory (both mapped and unmapped hydrologic features) • Store pertinent attributes and features associated with each study or unmapped feature.

  8. CNMS “Before”

  9. CNMS “After”

  10. Validation Elements • Study determined Unverified if: • One critical element fails, or • Four or more secondary elements fail • Elements assess change in Engineering study data, for instance: • Change in gage record • New or removed dam, reservoir, or levee • Change in Land use and land cover • High Water Marks • New or removed hydraulic structures (bridges, culverts) • Channel reconfiguration or improvements • New regression equations • Availability of new topo

  11. Critical Elements • Elements • Major Change in Gage Record • Updated and Effective Discharges Differ Significantly • Inappropriate Model Methodology • Addition / removal of a Major Flood Control Structure • Channel reconfiguration outside SFHA • 5 or More New or Removed Hydraulic Structures • Significant channel fill or scour • If one or more elements are true then Flood Hazard Information is invalid Yes = FAIL No = PASS

  12. Secondary Elements • Elements • Use of rural regression equations in urban area • Repetitive Losses outside SFHA • Increase of 50% or more in impervious area • 4 or less new or removed hydraulic structures • Channel Improvements / Shoreline Changes • Availability of better topographic / bathymetry • Changes in vegetation or landuse • Failure to identify Primary Frontal Dune • Significant storms with High Water Marks • New Regression Equations • If four or more elements are true then Flood Hazard Information is invalid

  13. Ohio CNMS Statistics • Zone AE Streams Analyzed: • Number of studies = 1,481 • Miles of studies = 15,411 • Counties = all 88 Need to Automate Processes!

  14. Automated Solutions Development Process • State the goal; list specific procedures required to achieve goal • Determine data inputs required • Customize data model • Create workflow • Develop tools • Test and adapt tools

  15. C1: Major change in gage record since effective analysis • For existing gages used in the effective FIS, is there a “new” peak discharge value > highest peak discharge value observed during the period of record used? • For existing or new gages*, is there a “new” peak discharge value > effective FIS 1% annual chance discharge? *DA study/DA gage must be between 0.5 and 1.5 for new gages

  16. C1 Example • FIS Data: • FIS lists hydrology study date as Nov 1996 • FIS gives 1% annual chance discharge as 110,000 cfs • USGS gage 08172000 used to establish discharge • Gage Data: Gage record for this gage includes October 18, 1998 discharge of 206,006 cfs, which is greater than effective discharge of 110,000 cfs. Therefore C1 “Fails”.

  17. C2: Updated and effective peak discharges differ significantly • Has the period of record for a gage increased > 25%, or is there a new gage now available? If so, does the newly calculated 1 % annual chance discharge vary significantly from the effective discharge?

  18. C1/C2 Data Inputs • Effective FIS Data • Existing gages used • End date of period record used • Period of record used • Drainage area of study • Effective 1% annual chance discharge closest to gage • USGS Gage Data • End date of gage period of record • Period of record • Drainage area of gage • Peak discharge data • New gages (need spatial data) • Flow Frequency Analysis Outputs

  19. Custom Gage Data Table • Effective FIS Data • USGS Gage Data • Flow Frequency Analysis Outputs • Derived/Calculated Fields

  20. C1/C2 Workflow – Automated Data Entry

  21. C1 Workflow

  22. C2 Workflow

  23. C1/C2 Tools – Model Builder

  24. C1/C2 Tool 1 – Model Builder

  25. C4: Addition/removal of a major flood control structure • Is > 30% of the drainage area for a study impacted by a new or removed dam(s)?

  26. C4 Procedure • Get downstream point of study • Using NHD stream network, trace upstream • Select all HUC12’s that intersect trace results (drainage area of study) • Select all NID dams within selected HUC12’s • Sum drainage area of selected dams that have a construction date > hydrology study date • Study fails if value is > 30% of study drainage area

  27. C4 Problems Encountered • Not all studies based on NHD stream network (downstream point needed to be manually moved to NHD line) • Trace upstream requires exactly one selected stream segment (downstream points at stream junctions a problem) • Resulting HUC12 drainage area of study does not always match FIS study drainage area • NID Data incomplete

  28. C4 Workflow

  29. C4 User’s Guide

  30. S1, 3, 7 Overview • Raster analysis • Scripted, rather than model-builder

  31. S1 Use of rural regression equations in urbanized areas • Check the FIS for analysis type: • If regression was not used to develop discharges, marked as passing. • S1 Tool • Determines % urban area in sub-watershed. • Checks against FEMA tolerance (15%) • Checks against regression type used. • If >15% (FEMA tolerance) and rural regression was used, does not pass. Joined back to S_Studies_Ln(as Yes/No) Simple tool – can process statewide

  32. S3 Increase in impervious area in the basin of more than 50% (for example, from 10% to 15%) • Analysis of land use data, if impervious area increases by 50% or more since Study Date, this element “Fails”. • Tool • Runs comparisons against multiple raster datasets to FEMA specified tolerances • Determines if there’s a significant change to HUC • Compares the % change to tolerance - can’t be greater than 50% • Calc’s results and joins to DB

  33. S7 30% change in land use within watershed since Study Date • Tool • Runs comparisons against multiple raster datasets to FEMA specified tolerances • Determines if there’s a significant change HUC • Compares the % change to tolerance (30%) - can’t be greater than 30%, did 3 or more land use types change significantly • Calc’s results and joins to STARR DB

  34. Input Data • HUC-12 Shapefile • Urban Change Indicator Raster • Land Use Raster (1992, 2001, Change) • Impervious Raster (2001)

  35. How the Tools Work • First script looks at the rasters and generates statistics for each HUC-12 boundary • This table is the basis for further analysis • S1 – Simply analyzes the amount of particular raster attributes (urban area), then checks against the regression field’s entry

  36. How the Tools Work • S3 - Impervious area over time. Examines table for each HUC in watershed, calculates % change in impervious areas. Over or under 50% change? • S7 – Land use change. Each land use code set to a binary category (1 or 0), examines the % change in types of land use to check for fast urbanization. Over or under 30% change? • Takes each check’s result and appends Pass/Fail attribute and reasoning to original linework.

  37. Workflow and Automation

  38. Raster emphasis • Model builder and scripting automation not limited to feature classes and tables. • Can combine features/shapefiles with raster inputs. • Useful for many types of widely-available data (NLCD, DEMs, precipitation)

  39. Time Savings • The tools rely on using intermediate C4 data to identify full watershed of target streams rather than re-calculating this. • Cuts out wait time on processing tasks. • No need to continually re-engage each step.

  40. CNMS Conclusions • CNMS Automated Solutions Provided • Increased accuracy • Tools automatically populated pass/fail status for each study based on criteria; no “typos” or missed records • Efficiency • Able to conduct tests on multi-county or state level • Could run tools overnight

  41. Automated Solutions:Final Tips and Tricks • Create detailed workflow; reorder steps to allow semi-automation if manual steps needed • Store intermediate data; have separate “final” dataset • Use “premade” tools and customize them to save time • Test, test, test! • Create user manual and/or document well

  42. Questions? • ODNR • Katherine Skalak 614-265-6709 Katherine.Skalak@dnr.state.oh.us • Stantec • Melissa Williams 614-486-4383 x3526 Melissa.Williams@stantec.com • Ryan Branch 614-486-4383 x3529 Ryan.Branch@stantec.com