Monotoring of instream habitats with focus on morphological dynamics based on Airborne Laser Bathymetry

Transcription

1 Monotoring of instream habitats with focus on morphological dynamics based on Airborne Laser Bathymetry Gottfried Mandlburger, Christoph Hauer, and Martin Wieser Vienna University of Technology Department of Geodesy and Geoinformation University of Natural Resources and Life Sciences, Vienna Water Management, Hydrology and Hydraulic Engineering

2 Airborne Laser (or LiDAR) Bathymetry (ALB) is a technique for measuring the depths of relatively shallow, coastal waters from the air using a scanning, pulsed laser beam. It is also known as Airborne Laser Hydrography (ALH) [ ]. *) Airborne Laser Bathymetry (ALB) *) C. Guenther et. al: MEETING THE ACCURACY CHALLENGE IN AIRBORNE LIDAR BATHYMETRY, EARSeL-SIG-Workshop LIDAR, Dresden, 2000 Laser source: λ=532nm, (Nd:Yag with frequency doubling) Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB RSGIS4HQ, 2

3 Study area: Pielach Neubacher Au Part of Natura2000 conservation area: Niederösterreichische Alpenvorlandflüsse, AT Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB RSGIS4HQ, 3

4 Example 3D point cloud (colored by reflectance) Neubacher Au, River Pielach, Data: Riegl LMS Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB RSGIS4HQ, 4

5 Cross-sectional comparison April-May-October m A steep bank gravel bar April gravel bank B side channel main channel dry side channel May October water surface Vegetation Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB RSGIS4HQ, 5

6 Data Processing - Geometry For each flight campaign (April/May/October 2013): Derivation of 3D point cloud from Scanner/GNSS/IMU data Strip adjustment and Quality control Derivation of Water Surface Model (semi-automatic approach) Range/Refraction correction of water echoes Classification of ground and river bed points DTM interpolation (0.5m grid) Quantification of river bed changes via (inter-epoch) difference models Statistical analysis and visualization of changes Cross-sectional comparison Color coded difference maps Histograms Point cloud High res. DTM Visualization and analysis of morphological changes Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB RSGIS4HQ, 6

7 Pielach Water depth (15.April 2013) Water depth [m] Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB RSGIS4HQ, 7

8 Changes of river bed topography : October - April Side channel filled Aggradation River cutting into the terrain Gravel bar aggradation Do changes Gravel bar in the river bed morphology degradation have an impact on hydro-morphological units and instream habitats? Difference [m] Degradation min max median -2.7m +1.6m 0.02 m Aggradation 2945 m³ Degradation 2259 m³ Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB RSGIS4HQ, 8

9 What is habitat modelling? Physically based models, which use the interaction between hydraulics / morphology and aquatic organisms (fish, macroinvertebrates) to determine quantitative habitat suitability. Water quality Water temperature habitat cover... Hydraulics Morphology Hydrology Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 9

10 Priniciples of habitat modelling functional or statistical linckage of abiotic and biotic Abiotic measurements (flow velocity, water depth, substrate mapping) numerical modelling (1D, quasi2d, 2D, 3D, sediment transport) statistical methods Biotic univariate (suitability- /preference curves) multivariate regression functional neuronal networks SI fuzzy-rules Environmental parameter water depth (m) Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 10

11 Habitat modelling on various scales (HEM) Macrohabitats (planform patterns) Mesohabitats (hydro-morphological units) (Montgomery & Buffington, 1997) (Nanson & Knighton, 1996) Microhabitats (e.g. local flow-velocity, sediment size) SI water depth (m) Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 11

12 Habitat modelling approach for the Pielach River Mesohabitat modelling: MEM-Concept Parasiewicz, (2001), Bisson & Montgomery (1996), Dollof et al. (1993), Kellerhals & Church (1989), Allen (1951): Riffles: shallow stream reaches with high current velocity, some surface turbulences and higher gradient. Small broken or unbroken standing waves. High Energy Fast runs: uniform fast flowing stream channels. Runs: monotone stream channels with well developed thalweg. The streambed: is longitudinal flat and laterally concave shaped. Moderate Energy Pools: Deep, slow or still region of water between riffle units. The flow type is scarcely perceptible flow. Shallow waters: low water depth habitats with low flow velocities. Low Energy Backwaters: quiet pools that accumulate on the side of a stream channel due to an obstruction or opposing current. Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 12

13 Model setup Pielach Model April = nodes / elements Model May = nodes / elements Model October = nodes / elements HN - modelling = depth-averaged two-dimensional model (finite volume / unstructured grid) Computational time for steady state conditions (Q input = Q output ) = 3.5 h (HQ 1 ) h (MJNQT) Total computational time = 9.25 h Calibrated roughness (low flow) Manning s n = Calibrated roughness (high flow) Manning s n = (main channel); n = variable for overbank areas ( ) data source: FHS Pielach Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 13

14 velocity depth shear stress Ybbs River fast run backwater riffle shallow water x,y <vel.dat> x,y <depth.dat> x,y <shear.dat> 1. routing: MH = [NC (d) + NC (v)] * NC (τ) <riffle> <pool> <run> <fast run> <low flow velocity*> 2. routing: depth criteria: NC(d) < g (4); NC(d) >/= g (4) <shallow w.> <backwater> 3. routing: x,y / x,y <shear.dat> <τ cr of d m> riffle pool NhmU/AhmU <riffle> stable [%,A] unstable [%, NhmU/AhmU <pool> stable [%,A] unstable [%,A] NhmU/AhmU <run> stable [%,A] unstable [%,A] NhmU/AhmU <fast run> stable [%,A] unstable [%,A] NhmU/AhmU <shallow w.> stable [%,A] unstable [%,A] NhmU/AhmU <backwater> stable [%,A] unstable [%,A] Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 14

15 Variability in habitat distribution according to discharge Low flow Mean flow Annual Flood (HQ 1 ) Juvenile habitats of target fish species Q = 1.82 Q = m 3 sm s -1-1 Neubacher Au upstream view Flow pulse Flood pulse shallow water habitats riffles Spawning of target fish species main channel unsuitable fast run habitats velocities > 1 ms -1 Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 15

16 Variability in habitat distribution according to morphological changes < HQ 1 discharge (m 3 s -1 ) discharge (m 3 s -1 ) HQ 30 HQ 5 HQ 1? April May Q = 6.52 m 3 s -1 Q = 6.52 m 3 s -1 Area (m²) April May October new riffle October erosion of riffle 0 riffle fast run run pool backwater shallow water TOTAL Q = 6.52 m 3 s -1 Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 16

17 Integrative Analysis Floods Directive / Water Framework Directive Flood hazard maps Habitat modelling Hinterleitner, 2004 (17) Development of river basin management plans under Directive 2000/60/EC and of flood risk management plans under this Directive are elements of integrated river basin management. The two processes should therefore use the mutual potential for common synergies and benefits, having regard to the environmental objectives of Directive 2000/60/EC, ensuring efficiency and wise use of resources while recognising that the competent authorities and management units might be different under this Directive and Directive 2000/60/EC. (18) Member States should base their assessments, maps and plans on appropriate best practice and best available technologies not entailing excessive costs in the field of flood risk management. L 288/28 EN Official Journal of the European Union (1) OJ L 311, , p. 3. Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 17

18 Summary / Conclusions Benefits of topo-bathymetric laser scanning Homogeneous and high point density within river bed and foreland (>10 pts/m2) Capability of capturing very shallow aquatic areas (depth < 50cm) Height accuracy ~10cm (error sources: ranging, GNN/IMU, water surface, refraction coefficient) ALS data provide high quality DTM data for instream habitat modelling on various scales (flow -and flood pulse studies) Important novel methodology to fulfill the aims of both the Floods- and the Water Framework Directive (e.g. wise use of resources) Small scale morphological changes (e.g. side erosion, gravel bar deposition) can be quantified high quality database for river research Development and validation of numerical models in hydraulic engineering Mandlburger, Hauer, Wieser: Monitoring of instream habitats based on ALB 18