The Land & Infrastructure Resiliency Assessment (LIRA) Project Economic Flood Hazard Assessment

Transcription

1 The Land & Infrastructure Resiliency Assessment (LIRA) Project Economic Flood Hazard Assessment Robert Armstrong / Cameron Kayter Agri-Environment Services Branch

2 LIRA Background Subcomponent of Climate Adaptation for Resilience in Agriculture project (Harvey Hill, AESB) Goal: provide decision makers at the watershed scale with support tools required for climate change/variability vulnerability and adaptation assessments. Support tools: How to guide for future pilot/watershed studies containing detailed methodologies and lessons learned from the research (version 1.0 available) Consists of 5 phases: Phase 3 (Past) first pilot study (RM of Corman Park, SK and area); economics, flood mapping methods Phase 4 (Present) refining methodologies, identifying further challenges Phase 5 (Future) transition to operational environments

3 LIRA is an Economic Study At the heart LIRA is an Economic Study that requires a flood hazard assessment The final product which supports decision making is a COST-BENEFIT ANALYSIS Damage Costs estimated for a Base Case (Status Quo) Potential Benefits and Cost Savings due to various adaptation options Cost Benefit analysis requires numerous inputs to feed the economic model Automated tools being developed For economic assessment (i.e. contract)

4 How Does LIRA Assess Flood Risk? 1. Map the landscape GIS 2. Get extremes information Climate, Hydrology - Vital economic, social and environmental infrastructure - Topography 3. Model/Predict the impacts 4. Identify, Develop and rank adaptation options Cost-Benefit Analysis < - Ag-Sector responses (riparian areas, BMP s?) - Infrastructure? - Policy/zoning? Other? * 25 year planning window 5. Reach informed decisions Steps 1-4 provides information in support of decision making Modeling & Economics Cost savings Flood Maps Damage Costs

5 Phase 4 LIRA: Exploring methodology in different parts of the Country *Landscape an important consideration for tools used* Nappan, Nova Scotia Assiniboine River Watershed, SK Redberry Lake Watershed, SK Typical prairie fill & spill hydrology Storm surge concerns Breaching of dykes Protection of valuable ag land Defined stream channels

6 Primary Modeling Challenge Primary Challenge: Cost Benefit analysis requires a spatial input of run-off/flooding extent Connectivity and associations between real world features matters Need to derive potential intersections of water with vital landscape components Driven by landscape characteristics; not just locations of permanent channels Solution: Can we identify vulnerable areas (i.e. hot spots) within the landscape using a simple modeling approach that can provide the spatial input? For Base Case? For Benefits/Cost savings with changes due to adaptation options?

7 Available Modeling Methods *Caution: need to generate spatial output* Standard run-off/flood modeling approaches considered: tied to theory and landscape characteristics; time and economic constraints? Hydrology (flow rates, peak flows, timing hydrographs at points along channels, at outlets); limited scope Free surface flows (1D - 3D models to simulate extent, depth, velocity, duration inundation areas associated with channels, dykes and dams); very complex tools Design flows (Culvert hydraulics within a drainage system); strict engineering Alternative landscape spatial run-off/flood modeling approach Consider characteristics of entire topography (ALL drainage paths and potholes) Directly model the FATE of spatial runoff over entire landscape Not a standard hydrological model

8 Possible Methodological Flow Chart Economic Analysis Spatial Modeling GIS Receptors Water extent Hydrological Modeling 1D 3D Free Surface Flow Modeling Drainage design; Culvert Hydraulics Ground Water Modeling???

9 Spatial Modeling Approach for the Prairie Pothole Region Overland flow simulation model coded in Fortran 95 by Dr. Kevin Shook (U of S, Centre for Hydrology) Designed for examining the physical connectivity within wetland environments and contributing area relationships; instrumental in developing a virtual basin modeling tool within the Cold Regions Hydrological Model platform Directly simulates the physical movement and spatial distribution of water over an entire landscape Directly simulates the fill and spill mechanism and movement of water between depressions, and within channels; all spatial scales possible, but no time step Uses a multiple direction flow algorithm (not steepest descent like the D8 method) Requires accurate surface elevation data: gridded Digital Elevation Model (DEM) Requires a depth of water to be applied to the DEM to simulate overland flow and accumulation Water depth can be obtained from extreme precipitation analysis or hydrological modeling Depths associated with extreme precipitation events (1:25, 1:50 1:200, 1:500 yr, or Vanguard) Depths associated with hydrological modeling (SWE spring runoff, rainfall-runoff response with varying antecedent conditions); Prairie hydrology special consideration

10 Value of Spatial Modeling Approach Provides a practical (simple), fast, and focused analysis of surface runoff and storage capacity within a landscape 1. Detailed elevation data can provide realistic landscape drainage patterns & identify any zones of water accumulation 2. Can produce snapshots of evolving water distribution during entire simulation 3. Can identify vulnerable locations (hot spots) that intersect with vital economic infrastructure or future planning initiatives etc. 4. Provides a required input for an economic assessment of damage cost estimates in vulnerable areas compared with costs of adaptation options

11 Value depends on data quality and scale

12 Validation Important Example: 30 m SRTM V3 void-free, floating point (NASA, JPL) Lower quality data can still be useful if there is sufficient topography & local knowledge & professional expertise is utilized Uncertainty in flat areas? Drainage paths and water bodies represented well

13 Limitations Quality of elevation data determines realism of water distribution Poor quality or coarse data produces unreliable spatial patterns DEM array size determines total simulation time; seconds to several days Not a standard hydrological or hydraulic model Does not include a time step, or directly consider hydrological processes Focuses on distribution of water and depth (where applicable), does not provide discharge rates, peak flows, duration or strictly modeled channel inundation Not designed for examining culvert hydraulics Workload increases with level of DEM detail (< 10 m scale) Road networks impound water (i.e. act as dams) Some DEM conditioning may be required to correct FATE of drainage need to know location of drainage structures (e.g. culverts and bridges)

14 Example of DEM Conditioning: drainage correction at culvert location

15 Example of Surface Runoff Modeling Smith Creek - High Yorkton quality elevation Creek - Moderate data 10 quality m LIDAR data 30 m SRTM

16 Value of Local Knowledge Are flood maps A realistic representation? Do model outputs of flooding hotspots make sense to local stakeholders?

17 Adaptation Scenario Considerations Base case used as a comparative reference for exploring potential adaptation strategies Examples Currently being considered in Assiniboine and for future - Zoning, wetland restoration, retention ponds; regulated/sustainable drainage Redberry Lake development policy guidelines (Zoning for future growth) Nappan (Reducing vulnerability to flood damages on adjacent Ag land - Testing dyke configurations and capacities)

18 Example of an adaptation option: wetland restoration - integrating berms

19 GIS - Receptor Database incorporating all data including surface run-off model outputs How Much of Parcel is under Water? GIS Analysis is done on a grid basis Economic output is done on a regional basis Water Fractioning - Percentage coverage of parcel under water - Differs depending on run-off scenario Challenges - Water depth results depends on elevation data - Doesn t ID where facilities are if not known Output database feeds into Economic model

20 Future Improvement/Challenge Integrate various modeling approaches to provide a comprehensive understanding of adaptation impacts 1. Spatial Component (LIRA) 2. Hydrology Component (hydrographs, processes) 3. Free Surface Flow Component (channel inundation) 4. Culvert Hydraulics (design capacity) 5. Big picture externalities

21 Big Picture Externalities: Consideration for downstream impacts?!

22 Summary LIRA is a methodology for decision makers that outlines steps for an economic flood hazard and adaptation assessment (potential implications for drought mitigation) Decision trees for cases where information is limited or detailed Landscape an important consideration for modeling strategy taken Requires spatial outputs for base case and adaptation options Exploring spatial runoff simulation model approach; positive feedback from end users More technical rigor possible by integrating hydrological information as input and combining modeling approaches Increased capacity needed to address complex modeling needs Increasing demand from watershed groups for comprehensive information No single tool capable of addressing complex flood modeling problems Need to consider capacity building and promotion of Community of Practice to address multi-dimensional modeling issues

23 Contact Information Acknowledgements Dr. Robert Armstrong Visiting Fellow National Agroclimate Information Service, AESB Agriculture and Agri-Food Canada Telephone: robert. Cam Kenny Erl Svendsen Aron Hershmiller Jesse Nielson Lyle Boychuk Dr. Kevin Shook Dr. Gordon Sparks Dr. Paul Christensen Tom Harrison John Kindrachuk Cameron Kayter A/LIRA Coordinator Agri-Environment Services Agriculture and Agri-Food Canada Telephone cameron.kayter@agr.gc.ca Flooding in the Assiniboine Watershed Photo Credit: AWSA