Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
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
Agenda • CNMS Background and Overview • Ohio CNMS Stats • Data Model • Automated Solutions
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
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
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
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.
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
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
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
Ohio CNMS Statistics • Zone AE Streams Analyzed: • Number of studies = 1,481 • Miles of studies = 15,411 • Counties = all 88 Need to Automate Processes!
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
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
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”.
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?
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
Custom Gage Data Table • Effective FIS Data • USGS Gage Data • Flow Frequency Analysis Outputs • Derived/Calculated Fields
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)?
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
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
S1, 3, 7 Overview • Raster analysis • Scripted, rather than model-builder
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
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
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
Input Data • HUC-12 Shapefile • Urban Change Indicator Raster • Land Use Raster (1992, 2001, Change) • Impervious Raster (2001)
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
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.
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)
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.
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
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
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