PC-based GIS tool for watershed modeling –KINEROS & SWAT (modular) Investigate the impacts of land cover change on runoff, erosion, water quality Targeted for use by research scientists, management specialists –technology transfer –widely applicable Introduction

Develop landscape assessment tool for land managers using indicators Advance scientific understanding of principles governing watershed response to change land cover changes in the US and associated impacts on runoff volume, water quality Investigate historical changes using repeat imagery (San Pedro, Catskill/Delaware) Investigate spatially distributed hydrologic processes using single scenes (Las Vegas) Forward looks with simulations Objectives of the Research

Introduction Hydrologic Modeling in GIS –The Shape and characteristics of the earth’s surface is useful for many fields of study. –Understanding how changes in the composition of an area will affect water flow is important!  What happens when residential development occurs?  How does this affect the watershed?  How can these affects be mitigated? –Best Management Practices (BMPs)

Introduction

Topographic Maps

A watershed boundary can be sketched by starting at the outlet point and following the height of land defining the drainage divides using the contours on a map. Outlet Point Traditional watershed delineation had been done manually using Contours on a topographic map. Introduction

Introduction: Terminology Drainage system - The area upon which water falls and the network through which it travels to an outlet. Drainage Basin - an area that drains water and other substances to a common outlet as concentrated flow (watersheds, basins, catchments, contributing area). Subbasin - That upstream area flowing to an outlet as overland flow Drainage Divide - The boundary between two basins. This is an area of divergent flow.

GIS Background

Raster Data Structure –Much of the data we will use in this class will be “Raster” data. –Raster formatted data is much more suitable for many types of landscape modeling, including hydrologic analysis. –Inputs such as elevation can only be processed as a raster data set –Raster is Faster, Vector is Corrector

GIS Background Raster Vector Real World

GIS Background: DEMs Digital Elevation Models (DEM) –A DEM is a digital representation of the elevation of a land surface. –X,Y and Z value –The USGS is the major producer of DEM’s in the Nation

GIS Background: DEMs DEM’s consist of an array of data representing elevation sampled at regularly spaced intervalsDEM’s consist of an array of data representing elevation sampled at regularly spaced intervals X Y ELEVATION VALUES

GIS Background: DEMs Two Scales of DEMs Available –1:24,000 Scale  Level meter spacing –Errors up to 15 meters inherent in data –Developed using automated methods from air photos –Systematic errors evident as banding –Not appropriate for hydrologic modeling  Level meter spacing –Matches map accuracy of 1:24,000 scale quads –Developed by scanning published quads –Appropriate for hydrologic modeling –1 Degree (~250,000) scale - 93 meter spacing  Appropriate for regional analysis (not for AGWA)

GIS Background: DEMs Preprocessing DEMs –DEMs typically require some type of preprocessing prior to hydrologic modeling to remove errors inherent in the data. This type of processing can greatly increase the accuracy of a DEM.. –Primary error found in DEMs are “Sinks”  A sink is an erroneous depression created by the DEM interpolation routine  Sinks are usually small and cause drainage basins to be incorrectly delineated

GIS Background: DEMs Stream 100-meter Elevation contour Sink Area 100 Cell Elevation Cells containing the contour are assigned the value of the contour, all other cells are interpolated. Sinks are always possible in areas where contours converge near a stream. An example Sink 100

Generating Surface Parameters –Flow across a surface will always be in the steepest down-slope direction –Known as “Flow Direction” this is the basis of all further watershed modeling processes. –Once the direction of flow is known it is possible to determine which and how many cells flow into any given cell! –This information is used to determine watershed boundaries and stream networks. GIS Background: Surface Parameters

Generating Surface Parameters - Flow Direction –In ArcView Spatial Analyst, the output of a Flow Direction is a grid whose values can range from 1 to 255 based on the direction water would flow from a particular cell. The cells are assigned valued as shown below. GIS Background: Flow Direction Target Cell

Generating Surface Parameters - Flow Direction –If a cell is lower than its eight neighbors, that cell is given the value of its lowest neighbor and flow is defined towards this cell. –If a cell has the same slope in all directions the flow direction is undefined (lakes) –If a cell has the same slope in multiple directions and is not part of a sink the flow direction is calculated by summing the multiple directions GIS Background: Flow Direction

Flow Direction Surface Original Surface

Generating Surface Parameters - Flow Accumulation –If we know where the flow is going then we can figure out what areas (cells) have more water flowing through them than others. –By tracing backwards up the flow direction grid we can figure the number of cells flowing into all cells in a study area –Accumulated flow is calculated as the accumulated number of all cells flowing into each downslope cell. GIS Background: Flow Accumulation

Generating Surface Parameters - Flow Accumulation –For an accumulation surface the value of each cell represents the total number of cells that flow into an individual cell –Cells that have high accumulation are areas of concentrated flow and may be used to identify stream channels. GIS Background: Flow Accumulation

Flow Direction Surface GIS Background: Flow Accumulation Flow Accumulation Surface

GIS Background: Flow Accumulation Flow Accumulation Surface DEM  Flow Direction  Flow Accumulation

AGWA

Integrated with US-EPA Analytical Tool Interface for Landscape Assessment (ATtILA) Simple, direct method for model parameterization Provide accurate, repeatable results Require basic, attainable GIS data –30m USGS DEM (free, US coverage) –STATSGO soil data (free, US coverage) –US-EPA NALC & MRLC landscape data (regional) Useful for scenario development, alternative futures simulation work. Objectives of AGWA

Natural Condition Decreased Vegetation Increased VelocityIncreased Runoff Increased Erosion Decreased Water Quality Land cover change Degradation Urbanization Woody plant invasion infiltration interceptionevapotranspirationsurface roughness soil moisture flood hazard groundwater recharge Land Cover & Hydrologic Response

Navigating Through AGWA Subdivide Watershed Into Model Elements SWATKINEROS generate rainfall input files Thiessen map from…  Gauge locations  Pre-defined continuous record Storm Event from…  Pre-defined return-period / magnitude  “Create-your-own” Intersect Soils & Land Cover Generate Watershed Outline

Navigating Through AGWA, Cont’d… Subwatersheds & Channels Continuous Rainfall Records prepare input data Run The Hydrologic Model & Import Results Display Results For subwatershed elements: Precipitation (mm) Evapotranspiration (mm) Percolation (mm) Surface Runoff (mm) Transmission Losses (mm) Water Yield (mm) Sediment Yields (t/ha) Channel & Plane Elements Event (Return Period) Rainfall For Plane & Channel Elements: Runoff (mm, m 3 ) Sediment Yield (kg) Infiltration (mm) Peak runoff (mm/hr, m 3 /sec) Peak sediment discharge (ks/sec)

ArcView working directory– secondary or temporary coverages, grids, and tables Spatial data– primarycoveragesand grids Simulation input/output– Separate directories for each simulation AGWA directory– primary tables, AV project file, and model executables Suggested File Structure for AGWA

Hydrologic Modeling & AGWA AGWA GIS Data Rainfall Runoff Erosion Assumptions

PROCESS runoff, sediment hydrograph time runoff STATSGO NALC, MRLC USGS 7.5' DEM Conceptual Design of AGWA Build Model Input Files Derive Secondary Parameters look-up tables Characterize Model Elements f (landcover, topography, soils) Discretize Watershed f (topography) View Model Results link model to GIS Build GIS Database PRODUCTS Contributing Source Area Gravelly loam Soil  Ks = 9.8 mm/hr  G = 127 mm  Por. = intensity time 10-year, 30-minute event

1992 NALC Hillshade DEM STATSGO Forest Oak Woodlands Mesquite Woodlands Grasslands Desertscrub Riparian Agriculture Urban Water Barren / Clouds Land Cover km N GIS Data Layers for AGWA  Upper San Pedro Basin, SE Arizona

CSA: 2.5% (6.9 km 2 ) 44 watershed elements 29 channel elements CSA: 20% (55 km 2 ) 8 watershed elements 5 channel elements CSA: 5% (13.8 km 2 ) 23 watershed elements 15 channel elements CSA: 10% (27.5 km 2 ) 11 watershed elements 7 channel elements km N  the influence of CSA on watershed complexity Automated Watershed Characterization Note channel initiation Point changing with CSA

km N pseudo- channel 11 channel 14 Abstract Routing Representation to channel 64 Watershed Configuration for SWAT  channel and subwatershed hydrology

Watershed Configuration for KINEROS km N Abstract Routing Representation  upland, lateral and channel elements in cascade

Characterizing the Watershed complex topography land cover soils high spatial variability complex watershed response

Characterizing the Watershed Homogeneous planes Hydrologic parameters represent intersections of topo., cover, soil Information loss as f (geometric complexity) Scaling issues Watershed modeling relies on condensing spatial data into appropriate units for representing processes leaves plenty of room for error!

BEGIN PLANE ID = 71, LEN = , AREA = SL = 0.029, MAN = 0.052, X = , Y = CV = 0.92, PRINT = 1 KS = 7.94, G = , DIST = 0.3, POR = 0.459, ROCK = 0.43 FR = 0.49, 0.33, 0.17, SPLASH = 24.42, COH = 0.006, SMAX = 0.93 INTER = 2.56, CANOPY = 0.133, PAVE = 0.00 END PLANE BEGIN PLANE ID = 72, LEN = 765.0, AREA = SL = 0.043, MAN = 0.054, X = , Y = CV = 0.93, PRINT = 1 KS = 7.77, G = , DIST = 0.3, POR = 0.459, ROCK = 0.43 FR = 0.49, 0.33, 0.16, SPLASH = 24.61, COH = 0.006, SMAX = 0.93 INTER = 2.85, CANOPY = 0.112, PAVE = 0.00 END PLANE BEGIN PLANE ID = 73, LEN = 945.0, AREA = SL = 0.038, MAN = 0.052, X = , Y = CV = 0.95, PRINT = 1 KS = 8.19, G = , DIST = 0.3, POR = 0.459, ROCK = 0.43 FR = 0.5, 0.33, 0.16, SPLASH = 24.91, COH = 0.006, SMAX = 0.93 INTER = 2.6, CANOPY = 0.137, PAVE = 0.00 END PLANE KINEROS Parameter Look-Up Table

NLCD Land coverABCDCover High intensity residential (22) Bare rock/sand/clay (31) Forest (41) Shrubland (51) Grasslands/herbaceous (71) Small grains (83) CURVE NUMBER Hydrologic Soil Group Curve Number From MRLC  Higher numbers result in higher runoff

N Contributing Source Area: 2000 acres - ~5% of total watershed area 20 subwatershed elements 19 channels STATSGO ID: AZ061 Grassland & desertscrub Moderate relief Sample Watershed Configuration - SWAT Watershed ID: 7 Area: 11.8 km2 Slope: 3.7 % Cover: 12.8 % Ks: 18.1 mm/hr CN: 71.8 Soil Hyd. Group: B Multiple Soil Horizons

N Contributing Source Area: 2000 acres - ~5% of total watershed area 33 planes - 7 upland elements - 25 lateral element 19 channels STATSGO ID: AZ061 Grassland & desertscrub Moderate relief Sample Watershed Configuration - KINEROS Watershed ID: 73 Area: 7.45 km2 Slope: 3.53 % Width: 945 m Length: 7876 m Interception: 2.60 mm Cover: % Manning's n: Pavement: 0.00 % Splash: Rock: 0.43 Ks: 6.67 mm/hr Suction: 115 mm Porosity: Max saturation: 0.93 Cv of Ks: 0.95 Sand: 50 % Silt: 33 % Clay: 17 % Distribution: 0.30 Cohesion: 0.006

rainfall depth (mm) Summer convective storm August 11, 2000 high spatial variability high temporal variability difficult to characterize flashy runoff response short duration (45 min) Winter frontal storm January 13, 2001 low spatial variability low temporal variability lead to little or no runoff long duration (3 hours) 0 5 km N Rainfall Characteristics in SE Arizona

What Could Possibly Go Wrong?? SYSTEMIC ERRORS These are “hidden” & include: Poor conceptual model Programming errors  AGWA, SWAT, KINEROS Poor process representation Errors in GIS data  Land cover, soils Assumptions in the look-up tables PROCESSING ERRORS These are “visible” & include: Errors in GIS data  DEM Lack of input data  GIS, rainfall AGWA fails to characterize watershed

Rainfall-Runoff Process in SE Arizona

Spatial Distribution of Rain Gauges # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # Typical Distribution Upper San Pedro Basin 6100 km 2 15 rain gauges High Density Watershed Walnut Gulch Exp. WS 148 km 2 89 rain gauges

 after Osborne et al., 1985** Design Storms stored in AGWA 5 yr 30 min 10 yr 30 min 100 yr 30 min 5 yr 60 min 10 yr 60 min 100 yr 60 min “create your own” Sample Design Rainfall Events for KINEROS Time (min) Intensity (mm/hr) 100-year, 60-minute 5-year, 30-minute 10-year, 60-minute ** Data reduced “on the fly” for watershed area

Limitations of GIS - Model Linkage Model Parameters are based on look-up tables - need for local calibration for accuracy Subdivision of the watershed is based on topography - prefer it based on intersection of soil, lc, topography No sub-pixel variability in source (GIS) data - condition, temporal (seasonal, annual) variability - MRLC created over multi-year data capture No model element variability in model input -averaging due to upscaling Most useful for relative assessment unless calibrated

AGWA Gone Awry Problems Running AGWA - DEM pre-processing  sinks  indeterminate boundary  converging flow  lack of defined flow path (big flat area) - User error  clicking on hill slope - Data coverage  no overlap in GIS data  suitability of GIS data f (scale, model)  when good software goes bad

30m DEM – Level I USGS - unfiltered, unfilled - contains many sinks - has poor drainage - watershed delineation fails 10m DEM from Air Photos - still contains some sinks - exhibits better drainage - boundary is correct - there are still internal failures 10m DEM improved - filtered using high filter - filled to remove 222 sinks - no sinks at the end - good drainage pattern - watershed succeeds; good boundary smoothing Watersheds Generated From Different DEMs

Same points, tolerances, settings note differences in results 30m DEM 10m DEM Parallel channels affect drainage Sinks interrupt flow Streams Generated From Different DEMs

Example of DEM Processing To Avoid Problems Problem: 30m DEM contains sinks, poor flow direction, and cannot create correct watershed Solution: Filter and fill the DEM before analysis Negative: streams running across the watershed divide boundary extends beyond the correct position smoothing can be good & bad Positives: better definition of channels no sinks hydrologic connectivity

Get the Best Available Data! USGS level 1 USGS level 2 inaccurate flow minimum CSA lower for Level II DEM error… banding better boundary bottom line junk in = junk out

User Error User selected a watershed outlet missed the channel and grabbed a separate basin could also fail to generate a watershed entirely user selected here should have selected over here AGWA Helps With This Problem - AGWA uses a search radius to find maximum flow accumulation - can move the point downstream - use a point coverage to specify outlet user selects here AGWA uses here

Lack of GIS Data Coverage Commensurate Land Cover, DEM, STATSGO - simple to determine - AGWA can handle small errors through averaging Land cover does not extend fully Watershed boundary

Relative vs. Absolute Change Availability of repeat classified imagery for change detection - NALC Calibration data set for absolute change analysis - USGS runoff gauging station - Internal validation preferable to calibrating solely on outlet Plenty of rainfall data for the time periods - NWS gauges - Potentially NEXRAD radar data Confounding effects of land cover change & rainfall data - Uniform vs. distributed rainfall

Extra Slides Follow

AGWA Processing Time Discretization level Watershed Area (km 2 ) Boundary Delineation CSA 20%CSA 10%CSA 2.5% 150 *0:030:220:250: :560:280:350: :180:481:131: :032:502:453: :035:375:436: :509:059:3010:36 * Data was clipped to a small buffer around watershed  benchmarks on a PIII, 866 MHz, 256 Mb RAM

Curve Number Modeling + Rainfall

Determining Curve Numbers