KINEROS2 and the AGWA Modeling Framework

Transcription

1 KINEROS2 and the AGWA Modeling Framework Darius J Semmens 1, David C Goodrich 2, Carl L Unkrich 2, Roger E Smith 3, David A Woolhiser 3, and Scott N Miller 4 1 U.S. EPA Office of Research and Development, Las Vegas 2 USDA-ARS Southwest Watershed Research Center, Tucson, AZ 3 USDA-ARS Retired, Fort Collins, CO 4 Univ. of Wyoming, Department of Renewable Resources, Laramie

2 Outline Background Overview Conceptual Model Numerical Model Sensitivity AGWA GIS Interface Conclusions

3 Background 1960 s - KINGEN KINEROS 2002 KINEROS AGWA Coupling with ArcView GIS Still event based, but developing continuous simulation w/ assimilated RS soil moisture

4 Overview Physically based Event model (continuous coming) Time step = minutes 1-D overland and channel flow Erosion and sediment transport Dynamic infiltration Distributed rainfall Flexible, distributed model elements GIS interface AGWA

5 K2 Conceptual Model Model Elements: Overland flow Urban overland Channels Detention structures (ponds) Injection Culverts

6 Conceptual Model - Structure Watershed approximated by cascade of overland-flow planes, channels, impoundments

7 Conceptual Model Overland Flow Overland-flow planes can be split into multiple components with different slopes, roughness, etc. Adjacent planes need not have the same width R ainfall C hannel Flow O ve rla n d F lo w Infiltration Infiltration in Channe

8 Conceptual Model Overland Flow Small scale spatial variability of infiltration represented in distribution sense and parameterized for numerical efficiency Microtopography can be represented

9 Conceptual Model Urban Element Urban element various runoff runon combinations

10 Conceptual Model - Infiltration Dynamic infiltration in K2 - interacts with both rainfall and runoff

11 Conceptual Model - Infiltration Infiltration with two-layer soil profile Soil moisture re-distribution during storm hiatus

12 Conceptual Model - Sediment Multiple particle class size sediment routing (noninteractive) Entrainment by raindrop impact and hydraulic shear

13 Conceptual Model - Channels Compound channel routing with distinct main and overbank channel infiltration

14 Ephemeral Streamflow Event of Aug. 27, 1982

15 Conceptual Model - Culverts Assumed to maintain free surface flow conditions at all times No lateral inflow Uses Darcy-Weisbach formula with kinematic assumption so that

16 Numerical Model Rainfall Uniform or distributed Time, depth/intensity Linear interpolation Uniform on individual elements Radar (AGWA) Interception Rainfall rate is reduced by the cover fraction until the amount retained reaches a specified interception depth.

17 Numerical Model - Infiltration Infiltrability Parlange 3-Parameter model Green and Ampt (1911) and Smith and Parlange (1978) are included as the two limiting cases 3 rd parameter, gamma, set to 0.85 Infilitrability depth approximation describes f c as a function of infiltrated depth, I, - eliminates the separate description of ponding time and the decay of f after ponding f c = K s 1+ γ γ I exp 1 G θi

18 Numerical Model - Infiltration Infiltration Small scale spatial variability accommodated by introducing a coefficient of variability in K s, CV K Assumes that K S is distributed log-normally f ( ) c re * 1 r ( γ Ie* = + ( e ) 1 e 1) 1+ 1, re * > 1 γ e* * 1/ c Closely describes ensemble infiltration behavior f e * and r e * are infiltrability and rain rate scaled on the ensemble effective asymptotic K S value. Has the effect of creating a gradual evolution of runoff rather than it commencing at the time of ponding

19 Numerical Model Overland Flow One-dimensional flow process in which a simple power relation relates flux to the unit-area storage Used in conjunction with the equation of continuity:

20 Numerical Model Overland Flow Boundary conditions Flow = 0 if upper boundary is a flow divide Equivalence of discharge between the upstream and downstream elements otherwise Roughness relationships Manning Chezy

21 Numerical Model Channel Flow Kinematic approximation again Continuity equation for a channel with lateral inflow is Compound channels Flows routed separately Explicit assumption of no energy transfer Implicit assumption of uniform water-surface elevation

22 Numerical Model Channel Flow Base flow user-specified constant base flow added at a fractional rate at each computational node along the channel Channel infiltration Optional At low flow an empirical expression is used to estimate an "effective wetted perimeter" for use in infiltration calculations, until a threshold depth reached

23 Numerical Model Sediment Optional rain-splash and hydraulic erosion computed for computed for upland, channel, and pond elements General mass-balance equation Net erosion, e, is a sum of splash-erosion rate as e s and hydraulic-erosion rate as e h, Splash erosion computed by Meyer and Wischmeier (1969) only when flow > 0

24 Numerical Model Sediment Hydraulic erosion/deposition modeled as a kinetictransfer process Transport capacity, C m, computed by modified version of Engelund and Hansen (1967) Transfer-rate coefficient, c g, Deposition set by particle settling velocity (Fair and Geyer, 1954) divided by the hydraulic depth, h Erosion limited for cohesive soils by v s /h

25 Numerical Solution Four-point implicit finite difference method Solutions obtained by Newton's method (Newton- Raphson technique) Unconditionally stable, but accuracy for routing is highly dependent on the size of x and t values used New version will set x and t for optimal solution

26 K2 SENSITIVITY Relative ranking of most sensitive inputs and parameters Rainfall Inputs (particularly in arid and semi-arid areas) Saturated Hydraulic Conductivity Hydraulic Roughness All a function of watershed geometric complexity

27 Sensitivity to Geometric Complexity the influence of CSA on watershed complexity CSA: 2.5% (6.9 km 2 ) 44 watershed elements 29 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 CSA: 20% (55 km 2 ) 8 watershed elements 5 channel elements N km

28 GIS-BASED HYDROLOGIC MODELING: THE AUTOMATED GEOSPATIAL WATERSHED ASSESSMENT TOOL Darius Semmens, Bill Kepner US EPA Landscape Ecology Branch Las Vegas, NV David Goodrich, Mariano Hernandez, Ian Burns, Averill Cate, Soren Scott USDA Agricultural Research Service Southwest Watershed Research Center Tucson, AZ

29 Project Background & Acknowledgements Long-Term Research Project Landscape Ecology Branch 6 years Interdisciplinary Watershed management Landscape ecology Atmospheric modeling Remote sensing GIS Multi-Agency USDA ARS US EPA University of Arizona University of Wyoming USGS Student Support 2 Post-Doc 2 PhD 2 Masters USDA-ARS David Goodrich Mariano Hernandez Averill Cate Ian Burns Casey Tifft Soren Scott US-EPA Bill Kepner Darius Semmens Dan Heggem Bruce Jones Don Ebert University of Arizona Phil Guertin University of Wyoming Scott Miller

30 Automated Geospatial Watershed Assessment (AGWA) Tool PC-based GIS tool for watershed modeling KINEROS & SWAT (modular) Investigate the impacts of land-use/cover change on runoff, erosion, and water quality at multiple scales Compare and visualize results Targeted for use by research scientists and management specialists Analyses can be integrated with those from other GISbased tools & data Widely applicable

31 Objectives of the AGWA tool Used 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 DEM Soil data (FAO soil map) Land-use/cover data (easily adapted to local data) Useful for scenario development, alternative futures simulation work.

32 Soil and Water Assessment Tool (SWAT) Daily time step Distributed: empirical and physically-based model Hydrology, sediment, nutrient, and pesticide yields Larger watersheds (> 1,000 km 2 ) Similar effort used by BASINS 71 Abstract Routing Representation to next channel channel 73 pseudochannel 71

33 AGWA Conceptual Design: Inputs and Outputs Watershed Delineation using Digital Elevation Model (DEM) Watershed Discretization (model elements) + Intersect model elements with Soils Land Cover Output results that can be displayed in AGWA KINEROS Outputs SWAT Outputs Channel Infiltration (m 3 /km) Plane Infiltration (mm) Runoff (mm or m 3 ) Precipitation (mm) ET (mm) Percolation (mm) Rain (Observed or Design Storm) Run model and import results Sediment yield (kg) Channel Disch. (m 3 /day) Peak flow (m 3 /s or mm/hr) Transmission loss (mm) Channel Scour (mm) Water yield (mm) Results Sediment discharge (kg/s) Sediment yield (t/ha)

34 AGWA ArcView Interface

35 Conceptual Design of AGWA PROCESS PRODUCTS Build GIS Database STATSGO NALC, MRLC USGS 7.5' DEM Discretize Watershed f (topography) Contributing Source Area Characterize Model Elements f (land cover, topography, soils) Gravelly loam Soil ¾ Ks = 9.8 mm/hr ¾ G = 127 mm ¾ Por. = intensity Derive Secondary Parameters look-up tables View Model Results link model to GIS runoff Build Model Input Files time 10-year, 30-minute event time runoff, sediment hydrograph

36 KINEROS2 Parameter Estimation Parameters based on soil texture (STATSGO, SSURGO, FAO) Texture Ksat Suction Porosity Smax CV Sand Silt Clay Dist Kff Clay Fractured Bedrock Clay Loam Sandy Clay Loam Silt Loam Sandy Loam Gravel Parameters based on land-cover classification (e.g. NLCD) Land Cover Type Interception (mm/hr) Canopy (%) Manning's n Forest Oak Woodland Mesquite Woodland Grassland Desertscrub Riparian Agriculture Urba n

37 AGWA Soil Weighting (KINEROS2) Area and depth weighting of soil parameters Intersection of model element with soils map Area weighting of averaged MUID values for each watershed element AZ061 Components for MUID AZ061 Component 2 45% Component 3 35% AZ067 AZ076 Component 1 20% Layers for component 3 Layer 1 2 Layer inches Layer 3 5

38 Parameter Manipulation (optional) Stream channel attributes Ksat Can manually change parameters for each channel and plane element Upland plane attributes Ksat

39 Visualization of Results Multiple simulation runs for a given watershed Calculate and view differences between model runs Color-ramping of results for each element to show spatial variability Automated tracking of simulation inputs

40 Spatial and Temporal Scaling of Results ¾ Using SWAT and KINEROS for integrated watershed assessment ¾ Land cover change analysis and impact on hydrologic response Upper San Pedro River Basin High urban growth Sierra Vista Subwatershed KINEROS Results Concentrated urbanization ARIZONA Phoenix # N # Tucson SONORA Water yield change between 1973 and 1997 <<WY >>WY SWAT Results 1997 Land Cover Forest Oak Woodland Mesquite Desertscrub Grassland Urban

41 Urbanization Effects (KINEROS2) Pre-urbanization 1973 Land cover Post-urbanization 1997 Land cover Results from pre- and post-urbanization simulations using the 10-year, 1-hour design storm event

42 KINEROS - Simulated runoff depth (distributed and uniform rainfall) 20 simulated runoff depth (mm) :1 line Rainfall Input uniform PPT distributed PPT observed runoff depth (mm)

43 Land-Cover Modification Tool Allows users to build management scenarios Location of land-cover alterations specified by either drawing a polygon on the display, or specifying a polygon map Types of Land-Cover Changes: Change entire user-defined area to new land cover Change one land-cover type to another in user-defined area Change land-cover type within user-supplied polygon map Create a random land-cover pattern e.g. to simulate burn pattern, change to 64% barren, 31% desert scrub, and 5% mesquite woodland

44 Alternative Futures: Base-Change Scenarios 1. CONSTRAINED Assumes population increase less than 2020 forecast (78,500). Development in existing areas, e.g. 90% urban. 2. PLANS Assumes population increase as forecast for 2020 (95,000). Development in mostly existing areas, e.g. 80% urban and 15% suburban. 3. OPEN Assumes population increase more than 2020 forecast (111,500). Most constraints on land development removed. Development occurs mostly into rural areas (60%) and less in existing urban areas (15%).

45 Percent Change in Runoff under Future Scenarios Relative change for each scenario can be mapped for each model output Highlights spatial variability in response to different scenarios One component of planning efforts (Derived from using future land covers and AGWA)

46 Plans

47 Limitations of GIS - Model Linkage Model Parameters are based on look-up tables - need for local calibration for accuracy - FIELD WORK! Subdivision of the watershed is based on topography - prefer it be 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

48 AGWA Support & Distribution Fact Sheets, Product Announcement, Brochures Documentation and User Manual Quality Assurance Report Research Plan Code Structure (Avenue Scripts, Dialogs, System Calls) EPA and USDA/ARS companion Websites Journal Publications (Hernandez et al. 2000, Miller et al. 2002a, Miller et al. 2002b, Kepner et al., 2004) Training: Las Vegas (2001); Reston (2002); Tucson (2003); San Diego (2004) AGWA Web Sites

49 CONCLUSIONS KINEROS2 Evolved from a research model to one gaining wider applicability Physically based, distributed, event model Most sensitive to rainfall input and K s, but model geometric complexity also significant GIS interface, AGWA, is key to developing complex spatial organization and parameter inputs AGWA facilitates calibration, change analyses, and planning work through scenario development