Terrain Analysis Tools for Routing Flow and Calculating Upslope Contributing Areas John P. Wilson Terrain Analysis for Water Resources Applications Symposium.

Slides:



Advertisements
Similar presentations
Spatial Analysis with ArcView: 2-D. –Calculating viewshed –Calculating line of sight –Add x and y coordinates –Deriving slope from surface data –Deriving.
Advertisements

Digital Elevation Model (DEM) Resolution and Stream Extraction Using Terrain Openness Josh Page and Dr. Wei Luo, Northern Illinois University, Department.
The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.
Standard watershed and stream delineation recipe - Vector stream (ex. NHD data) fusion into DEM raster (burning in) - Sink removal - Flow direction - Flow.
CEE 795 Water Resources Modeling and GIS Learning Objectives: Perform raster based network delineation from digital elevation models Perform raster based.
Digital terrain analyses – DEM principles DEM data structure Lines – contour map Raster – altitude matrix Vector – TIN/Triangular irregular network Vector.
Watershed Delineation and Characteristics on Alaska’s North Slope Matt Khosh University of Texas at Austin Department of Marne Science.
Topographic Maps.
3D and Surface/Terrain Analysis
Exploring Earth’s Surface Chapter 1 Section 4. Standard  6.2 Topography is reshaped by the weathering of rock and soil and by the transportation and.
Topographic Maps. 1.This is what you call the vertical distance between the contour lines. 2.These connect points of equal elevation. 3.Gently sloping.
CS 128/ES Lecture 12b1 Spatial Analysis (3D)
CS 128/ES Lecture 12b1 Spatial Analysis (3D)
Alpine3D: an alpine surface processes model Mathias Bavay WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland.
WFM 6202: Remote Sensing and GIS in Water Management
Lecture 16 Terrain modelling: the basics
Digital Elevation Model based Hydrologic Modeling Topography and Physical runoff generation processes (TOPMODEL) Raster calculation of wetness index Raster.
Concept Course on Spatial Dr. A.K.M. Saiful Islam Application of GIS in Watershed Analysis Dr. A.K.M. Saiful Islam Institute of Water and Flood.
Week 10. GIS Data structure II
Some Potential Terrain Analysis Tools for ArcGIS David G. Tarboton
Flow modeling on grid terrains. DEM Representations TIN Grid Contour lines Sample points.
Remote Sensing and GIS in Water Dr. A.K.M. Saiful Islam Hands on training on surface hydrologic analysis using GIS Dr. A.K.M. Saiful Islam.
From Topographic Maps to Digital Elevation Models Daniel Sheehan IS&T Academic Computing Anne Graham MIT Libraries.
The Global Digital Elevation Model (GTOPO30) of Great Basin Location: latitude 38  15’ to 42  N, longitude 118  30’ to 115  30’ W Grid size: 925 m.
Terrain Mapping and Analysis
FNR 402 – Forest Watershed Management
Focal and Zonal Functions RNR 419/519. Focal Functions Focal (or neighborhood) functions compute an output grid in which the output value at each cell.
Step 1: Assess Riparian Resource Function Using PFC §1d. Complete PFC assessment l 17 questions about attributes and processes l Reminder – PFC based on:
A Simple Drainage Enforcement Procedure for Estimating Catchment Area Using DEM Data David Nagel, John M. Buffington, and Charles Luce U.S. Forest Service,
1 GIS in Hydrology Watershed management Definitions Algorithms Watershed delineation Automatically delineating watersheds Flow length Raster to vector.
Intro. To GIS Lecture 9 Terrain Analysis April 24 th, 2013.
Chapter 3 Section 1- Finding Locations on Earth
STRATIFICATION PLOT PLACEMENT CONTROLS Strategy for Monitoring Post-fire Rehabilitation Treatments Troy Wirth and David Pyke USGS – Biological Resources.
Model Construction: interpolation techniques 1392.
Soil Movement in West Virginia Watersheds A GIS Assessment Greg Hamons Dr. Michael Strager Dr. Jingxin Wang.
Creating Watersheds and Stream Networks
Sarah Giles Holly Kuestner Steven Orr Qi Zhang. 1.Impervious Surfaces’ Effects on Flow Accumulation (Holly) 2.Variable Source Area (Holly) 3.Catchment.
Esri UC 2014 | Technical Workshop | Creating Watersheds, Stream Networks and Hydrologically Conditioned DEMS Steve Kopp Dean Djokic.
Adding the third dimension In high relief areas variables such as altitude, aspect and slope strongly influence both human and physical environments –a.
An Improved Global Snow Classification Dataset for Hydrologic Applications (Photo by Kenneth G. Libbrecht and Patricia Rasmussen) Glen E. Liston, CSU Matthew.
L7 - Raster Algorithms L7 – Raster Algorithms NGEN06(TEK230) – Algorithms in Geographical Information Systems.
Chapter 10 Spatial Data Models. Introduction n The Earth: complex, multivariate system n Computer processing of geo-referenced data in GIS n Discretization.
DTM Applications Presentation.
General Introduction. Developed by USGS Freely available via Internet
Models of the Earth Section 3 Section 3: Types of Maps Preview Key Ideas Topographic Maps Topographic Maps and Contour Lines Index Contour, Contour Interval,
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Viewshed Analysis A viewshed refers to the portion of the land surface that is visible from one or more viewpoints. The process for deriving viewsheds.
Ecosystem Model Evaluation
Western Mensurationists Meeting 2016
Flow field representations for a grid DEM
Grid-Based Modeling with Digital Elevation Models
Factsheet #11 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Small Stream Mapping Method: Local.
Terrain modelling: the basics
Digital Terrain Analysis for Massive Grids
Terrain Analysis Using Digital Elevation Models (TauDEM) in Hydrology
Incorporating Ancillary Data for Classification
Digital Elevation Model Based Watershed and Stream Network Delineation
A Geographic Information System Tool for Hydrologic Model Setup
Digital Elevation Model Based Watershed and Stream Network Delineation
Digital Elevation Models and Hydrology
Spatial Analysis & Modeling
Terrain Analysis Using Digital Elevation Models (TauDEM)
TOPMODEL and the role of topography and variable contributing areas in runoff production Learning objectives Be able to describe the topographic wetness.
May 18, 2016 Spring 2016 Institute of Space Technology
Digital Elevation Model based Hydrologic Modeling
Problem: Interpolation of soil properties
Environmental Modelling with RASTER DEMs: Hydrologic Features
Digital Elevation Models and Hydrology
Lecture 06: Digital Terrain Analysis II
Creating Watersheds and Stream Networks
Presentation transcript:

Terrain Analysis Tools for Routing Flow and Calculating Upslope Contributing Areas John P. Wilson Terrain Analysis for Water Resources Applications Symposium 2002

Today’s Topics Guiding principles Guiding principles Proposed flow routing algorithms Proposed flow routing algorithms Flow routing methods implemented in TAPES-G Flow routing methods implemented in TAPES-G Sensitivity of computed topographic attributes to choice of flow routing method Sensitivity of computed topographic attributes to choice of flow routing method Key decisions, problems, and challenges Key decisions, problems, and challenges

Scales / Processes / Regimes Global Meso Topo Micro Nano Cloud cover and CO2 levels control primary energy inputs to climate and weather patterns Prevailing weather systems control long-term mean conditions; elevation-driven lapse rates control monthly climate; and geological substrate exerts control on soil chemistry Surface morphology controls catchment hydrology; slope, aspect, horizon, and topographic shading control surface insolation Vegetation canopy controls light, heat, and water for understory plants; vegetation structure and plant physiognomy controls nutrient use Soil microorganisms control nutrient recycling

Water Flow on Hillslopes

Land Surface Shape Courtesy Graeme Aggett 2001

Terrain Shape … Terrain shape / drainage structure important at toposcale Terrain shape / drainage structure important at toposcale Locally adaptive gridding procedures work well with contour and stream line data Locally adaptive gridding procedures work well with contour and stream line data Need filtering / interpolation methods that respect surface structure for remotely sensed elevation sources Need filtering / interpolation methods that respect surface structure for remotely sensed elevation sources Choose resolution based on data sources / quality and not the application at hand Choose resolution based on data sources / quality and not the application at hand

Flow Direction / Catchment Area Flow direction shows path of water flow … Flow direction shows path of water flow … Upslope contributing area A is area of land upslope of a length of contour l Upslope contributing area A is area of land upslope of a length of contour l Specific catchment area is A/l Specific catchment area is A/l

Proposed Flow Routing Algorithms Vary depending on granularity with which aspect is computed and whether single or multiple flow paths are allowed Vary depending on granularity with which aspect is computed and whether single or multiple flow paths are allowed Single flow direction algorithms Single flow direction algorithms D8 (O’Callaghan and Mark 1984) D8 (O’Callaghan and Mark 1984) Rho4 / Rho8 (Fairfield and Leymarie 1991) Rho4 / Rho8 (Fairfield and Leymarie 1991) Aspect-driven (Lea 1992) Aspect-driven (Lea 1992)

… Flow Routing Algorithms (2) Multiple flow direction algorithms Multiple flow direction algorithms FD8 (Quinn et al. 1991) FD8 (Quinn et al. 1991) FMFD (Freeman 1991; Holmgren 1994) FMFD (Freeman 1991; Holmgren 1994) DEMON (Costa-Cabral and Burges 1994) DEMON (Costa-Cabral and Burges 1994) R.flow (Mitasova and Hofierka 1993; Mitasova et al. 1995, 1996) R.flow (Mitasova and Hofierka 1993; Mitasova et al. 1995, 1996) D∞ (Tarboton 1997) D∞ (Tarboton 1997) Form-based method (Pilesjo et al. 1998) Form-based method (Pilesjo et al. 1998) Courtesy Qiming Zhou and Xuejun Liu 2002

TAPES-G Algorithms Single-flow-direction D8 method Single-flow-direction D8 method Randomized single-flow-direction Rho8 method Randomized single-flow-direction Rho8 method Multiple-flow-direction FD8 and FRho8 methods Multiple-flow-direction FD8 and FRho8 methods DEMON stream-tube method DEMON stream-tube method

TAPES-G Inputs Square-grid DEM Square-grid DEM Important decisions about extent of study area and how to handle edge effects, spurious sinks or pits, etc. Important decisions about extent of study area and how to handle edge effects, spurious sinks or pits, etc. Interested in hydrologic connectivity of topographic surface Interested in hydrologic connectivity of topographic surface

TAPES-G Outputs

Final Cottonwood Creek DEM

Aspect / Primary Flow Direction? Shows aspect computed using finite difference method Shows aspect computed using finite difference method Poor choice of scale bar? Poor choice of scale bar?

Primary Flow Direction (FLOWD) Approximate surrogate for aspect since it identifies direction to the nearest neighbor with maximum gradient Approximate surrogate for aspect since it identifies direction to the nearest neighbor with maximum gradient FLOWD = 2 j - 1 where j = arg max i = 1,8 i = 1,8 The approximate aspect corresponding to this flow direction is Ψ D8 = 45j The approximate aspect corresponding to this flow direction is Ψ D8 = 45j

D8 SFD Algorithm Does well in valleys Does well in valleys Produces many parallel flow lines and problems near catchment boundary Produces many parallel flow lines and problems near catchment boundary Cannot model flow divergence in ridge areas Cannot model flow divergence in ridge areas

D8 SFD Algorithm Diagram shows detail near catchment boundary Diagram shows detail near catchment boundary Dark cells not located on boundary – due to subtle change in aspect as it swifts from south to southeast Dark cells not located on boundary – due to subtle change in aspect as it swifts from south to southeast

Rho8 SFD Algorithm Breaks up parallel flow paths / produces mean flow direction equal to aspect Breaks up parallel flow paths / produces mean flow direction equal to aspect More cells with no upslope connections More cells with no upslope connections Produces unique result each time Produces unique result each time

FD8 MFD Algorithm Distributes flow on hillslopes to each downslope neighbor on a slope-weighted basis Distributes flow on hillslopes to each downslope neighbor on a slope-weighted basis Specify cross-grading threshold to disable this feature in valleys Specify cross-grading threshold to disable this feature in valleys

FD8 Flow Dispersion Weights

DEMON Algorithm Flow generated at each source pixel and routed down a stream tube until edge of DEM or a pit is encountered Flow generated at each source pixel and routed down a stream tube until edge of DEM or a pit is encountered Stream tubes constructed from points of intersections of a line drawn in gradient direction and a grid cell edge Stream tubes constructed from points of intersections of a line drawn in gradient direction and a grid cell edge

DEMON Stream-Tube Algorithm Three variants used in TAPES-G – related to … Three variants used in TAPES-G – related to … Choice of DEM Choice of DEM Use of grid centroids in place of vertices Use of grid centroids in place of vertices Definition of aspect Definition of aspect

Upslope Contributing Area Computed with contour-based stream tubes in northern part of catchment … Computed with contour-based stream tubes in northern part of catchment …

TAPES-C Element Network

Contour DEM Elements Set of elements formed by contours and flow lines Set of elements formed by contours and flow lines Proceeding uphill, flow lines are terminated (A) and added (B, C) to maintain even spacing Proceeding uphill, flow lines are terminated (A) and added (B, C) to maintain even spacing Lines are constructed using either a minimum distance (BD) or orthogonal (CE) criterion Lines are constructed using either a minimum distance (BD) or orthogonal (CE) criterion

Specific Catchment Area 105 km 2 Squaw Creek catchment in Gallatin National Forest, Montana 105 km 2 Squaw Creek catchment in Gallatin National Forest, Montana Results derived from 30 m DEMS for 3 USGS 1:24,000 scale map quadrangles Results derived from 30 m DEMS for 3 USGS 1:24,000 scale map quadrangles

Specific Catchment Area Maps

Secondary Topographic Attributes

Sediment Transport Capacity Index

Grid Comparisons

Key Decisions and Challenges Methods can be distinguished based on equation used to estimate aspect and whether or not they permit flow to two or more downslope cells Methods can be distinguished based on equation used to estimate aspect and whether or not they permit flow to two or more downslope cells Most of the results produced thus far relate to coarse resolution DEM products Most of the results produced thus far relate to coarse resolution DEM products Sensitivity analysis results are difficult to extrapolate to new study sites Sensitivity analysis results are difficult to extrapolate to new study sites

New Data Sources Several presentations about SAR and LIDAR technology data at this conference Several presentations about SAR and LIDAR technology data at this conference Must develop and/or find methods for filtering and interpolation that respect surface structure for these remotely sensed elevation sources Must develop and/or find methods for filtering and interpolation that respect surface structure for these remotely sensed elevation sources

Interpolation Results TINIDW Thin plate splineTOPOGRID Surf.tps (GRASS) Courtesy Graeme Aggett 2001

Better Sensitivity Analyses?

Topographic Attributes Elevation Elevation Slope Slope Profile curvature Profile curvature Plan curvature Plan curvature Distance from ridge lines Distance from ridge lines Incident solar radiation Incident solar radiation Topographic wetness index Topographic wetness index Sediment transport capacity index Sediment transport capacity index

Fuzzy Classification Split study area into three equal parts Split study area into three equal parts Took stratified random sample and Took stratified random sample and extracted topographic attributes Performed several fuzzy k-means classifications Performed several fuzzy k-means classifications Calculated confusion index and F and H parameters and generated fuzzy and crisp landform class maps Calculated confusion index and F and H parameters and generated fuzzy and crisp landform class maps

Final Landform Classes Valley bottoms Valley bottoms Main drainage lines Main drainage lines Lower slopes Lower slopes Steep, shaded north-facing slopes Steep, shaded north-facing slopes Narrow ridge lines Narrow ridge lines Steep, south-facing, drier upper slopes and broad ridges Steep, south-facing, drier upper slopes and broad ridges

Cluster Centers and Ranges

Summary Data for Six Classes

Final Map?

Closing Comments Several graduate students working on new data sources and fuzzy classification of landscapes Several graduate students working on new data sources and fuzzy classification of landscapes One is looking at performance of five flow routing algorithms in different landform classes with 5 m SAR DEM for example One is looking at performance of five flow routing algorithms in different landform classes with 5 m SAR DEM for example May be able to answer one or two questions if there is time available May be able to answer one or two questions if there is time available