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Hawaii Pacific GIS Conference 2012: Water Resources - Hawaii's Fluvial Systems: Using GIS to Assess Current Conditions and Identify Management Strategies in a Changing Climate
 

Hawaii Pacific GIS Conference 2012: Water Resources - Hawaii's Fluvial Systems: Using GIS to Assess Current Conditions and Identify Management Strategies in a Changing Climate

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    Hawaii Pacific GIS Conference 2012: Water Resources - Hawaii's Fluvial Systems: Using GIS to Assess Current Conditions and Identify Management Strategies in a Changing Climate Hawaii Pacific GIS Conference 2012: Water Resources - Hawaii's Fluvial Systems: Using GIS to Assess Current Conditions and Identify Management Strategies in a Changing Climate Presentation Transcript

    • Hawai’i’s fluvial systems: Using GIS to assesscurrent conditions and identify management strategies in a changing climate Ralph W. Tingley III1, Dana M. Infante1 , Richard A. MacKenzie2, Robert Nishimoto3, James Parham4 1 Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 2 Institute of Pacific Island Forestry, Pacific Southwest Research Station, Hilo, HI 3 Division of Aquatic Resources, Honolulu, HI 4 Parham and Associates Environmental Consulting, Gallatin, TN
    • Degraded habitat and aquatic biodiversity• Habitat degradation is the main cause of decline and extinction of freshwater fishes (Helfman 2007) – Contributed to 71% of all known fish extinctions• Other organisms’ declines assumed related to habitat – In US and Canada, 72% of mussel and 47% of crayfish taxa imperiled (Abell et al. 2000, Taylor et al. 2000) – 43% of stoneflies, 36% of amphibians, and 18% of dragon/damselflies imperiled in U.S. (Stein et al. 2000)
    • www.fishhabitat.orgMission: To protect, restore and enhance the nation’s fish and aquatic communities through partnerships that foster fish habitat conservationInitial objectives (Partial list) • Conduct a condition analysis of all fish habitats • Identify priority fish habitatsCompleted for conterminous U.S., Alaska andHawai’i in 2010 • Followed a landscape approach
    • The landscape approach• Natural and anthropogenic landscape factors influence habitat Natural Human factors land uses• Habitat condition directly influences stream biota Habitat• Landscape disturbances are assumed to impact stream biota Stream organisms
    • The landscape approach• For conterminous U.S…. Natural Human factors land uses % Network Pasture Habitat Stream organisms % Network Urban Med Esselman et al. 2011
    • Assessing the relative risk of degradation• Catchment landscape factors are good predictors of aquatic biology and physical habitat (Gergel et al. 2002; Allan 2004) Norris et al. (2001)) Danz et al. (2007))• Useful for assessing large regions across which site-specific data may be unavailable or inconsistently collected • Can compare relative risk of degradation across stream reaches
    • Hawai’i 2010 initial assessment overview• Created spatial framework at two scales, local and network catchment• Identified variables indicating major sources of disturbance• Attributed and aggregated data local and network catchments• Grouped variables and created sub-indices for specific disturbance categories, relativized and summed into a cumulative index (follows Danz et al. 2007) – Urban land cover and population density – Agriculture – Barriers to fish movement (fragmentation) – Point source disturbances – Length of ditches – Former plantations – 303D listed streams
    • Spatial framework and database development • National Hydrography Dataset (1:24k NHD) – Stream arcs only – Canal/ditches and pipelines not included – New breaks implemented based on elevation (Parham and Lapp 2006)
    • Spatial framework and database development • National Hydrography Dataset (1:24k NHD) – Local and network catchments delineated using ArcHydro – Represents an improvement to any existing spatial framework – Each reach had landscape data summarized at both scales – Data must be continuous Network and available state-wide NHD 1:24K catchment Reach Local catchment
    • Disturbance variables:Urban and point source inputs of toxics and other pollutants
    • Disturbance variables:Former plantation land, current agriculture, stream fragmentation, ditches, 303D listings• At local and network scale, sub-index scores generated using Principal Components Analysis (PCA) or additive methods• Disturbance scores at local and network catchments summed to obtain relative condition score
    • Current applications• Several agencies are using the spatial framework to organize and attribute data.• Scores are being incorporated into the Atlas of Hawaiian Watersheds• Condition assessment of coastal habitat and reefs ©2008 Atlas of Hawaiian Watersheds & Their Aquatic Resources.
    • What’s next for the assessment?• Streams respond to disturbances differently Natural Human due to differences in factors land uses natural factors (Melles et al. 2011, Utz et al. 2011)• Accounting for these Habitat natural differences allows for an increased understanding of relative condition Stream organisms
    • Classification of ecological potential: Goal and objectives• Develop an ecological classification of Hawaiian stream reaches that characterizes their natural potential for supporting native species 1. Identify biologically relevant landscape-scale variables 2. Identify biologically relevant climate variables 3. Identify where changes in species composition occur in relationship to climate and biological variables
    • Step 1: Identify biologically relevant landscape variables • Presence/absence data Identify initial natural Compile is available across landscape biological data islands variables Summarize local/network • Only the most catchments Presence/ influential landscape absence data variables will be Use multivariate included in analysis approach to select variablesOutput: Biologicallyrelevant landscape variables
    • Natural landscape variables and datasetsVariable Description Scale/Res. Scale of summary SourceDrainage area Total area draining to stream reach 1:24k Upstream catchment HFHP modified NHDStream order Strahler stream order 1:24k Local reach HFHP modified NHDSlope Precent slope 4 m grid All reach scales IFSAR elevation data-NOAAMaximum slope drop Maximum drop in slope downstream of reach 4 m grid Downstream reach IFSAR elevation data-NOAADistance Inland Linear distance from marine input 1:24k Downstream reach HFHP modified NHDElevation zones Biologically significant breaks in elevation 1:24k Local reach Parham and Lapp 2006 CWRM data/IFSAR elevation data-Groundwater delivery Mean groundwater delivery to reach Local/upstream catchment NOAA; In development*Warm groundwater input Area of catchment with warm groundwater input 10m grid Local/upstream catchment GeothermEx, Inc.*Soil erodability Mean soil erodability within catchment variable Local/upstream catchment SSURGODataset*Major land resource areas Terrestrial based landscape descriptions 10m grid Local/upstream catchment USDA/NRCS/NHQ/RAD*Geologic age Age of hawaiian islands soils/rock variable Local/upstream catchment USGS*Geologic type Geolocial type of all Hawaiian islands variable Local/upstream catchment USGS*Soil type Type of Hawaiian soils variable Local/upstream catchment SSURGO*Datasets aquired through the Hawaii statewide GIS program
    • Step 2a: Identify biologically relevant climate variables: linking biology to flow Identify initial Compile Identify reaches natural biological data with landscape flow data variables Summarize Use multivariate local/network approach to catchments Presence/ select variables absence data Use multivariate Regional Biologically approach to species relevant flow select variables datasets variablesOutput: Biologicallyrelevant landscape variables
    • Atyoida bisulcata of the Hilo/Hamakua Coast On-going research of: Richard MacKenzie, Ralph Tingley, Ayron Strauch, Dana Infante, Greg Bruland, Patra Foulk, Therese Frauendorf• Extreme precipitation gradient• 9-15 sites – Flow data – Body condition – Length – Relative abundance – Fecundity
    • Atyoida bisulcata of the Hilo/Hamakua Coast• Variables that show declines with decreasing annual flow: – Length – Body condition – Relative abundance
    • Step 2b: Identify biologically relevant climate variables: linking flow to climate Identify initial Identify Compile Identify reaches natural biological data with climate landscape flow data variables variables Summarize Use multivariate Summarize local/network approach to local/network catchments Presence/ select variables catchments absence data Use multivariate Regional Biologically approach to Link climate to species relevant flow select variables flow variables datasets variablesOutput: Biologically Output: Biologicallyrelevant landscape relevant climate variables variables
    • Climate variablesVariable Description Scale/Res. Scale of summary Source*Solar radiation Estimated daily solar insulation contours 10m grid Local/network catchment Department of PlanningMean annual precipitation Mean annual precipitation 250 m grid Local/network catchment 2011 Rainfall Atlas of HawaiiMean dry season precipitation April through October 250 m grid Local/network catchment 2011 Rainfall Atlas of HawaiiMean wet season precipitation November through March 250 m grid Local/network catchment 2011 Rainfall Atlas of HawaiiVariance of monthly precipitation Entire water year 250 m grid Local/network catchment 2011 Rainfall Atlas of HawaiiVariance of monthly precipitation April though October 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaiiwet seasonVariance of monthly precipitation November through March 250 m grid Local/network catchment 2011 Rainfall Atlas of Hawaiidry seasonMean annual air temperature Mean annual air temperature 10 m grid Local/network catchment PRISM dataDaily rainfall metrics Number of consecutive days with no rain*Datasets aquired through the Hawaii statewide GIS program • Initial climate variables are being selected based on availability and anticipated predictions
    • Step 3: Identify breaks in relevant variables and classify streams Biologically relevant Biologically relevant landscape variables climate variables Presence/ Regional absence datasets Establish breaks through: 1. CART analysis 2. Regional studies Classification of ecological potential
    • Benefit and application of results• Increased utility of the NFHAP current condition assessment – Reference of community structure at reach scale• Contribution of a new scale of classification to the Atlas of Hawaiian Watersheds• Act as a baseline for understanding vulnerability to changes in climate
    • Species specific vulnerability to climate change: Atyoida bisulcata
    • Species specific vulnerability to climate change: Atyoida bisulcata
    • Acknowledgements• Dr. Christian Giardina, Dr. Gordon Smith, Dr. Ayron Strauch, Dr. Greg Bruland• Patra Foulk, Therese Frauendorf• Arthur Cooper, Dan Wieferich, Jacqui Fenner, Ali David, Dr. Yin-Phan Tsang• USDA Forest Service• Division of Aquatic Resources• Hawai’i Fish Habitat Partnership• University of Hawai’i at Manoa• Kamehameha Schools• Michigan Sate University• USGS• USFWS
    • Mahalo!