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PROCESS-BASED, DISTRIBUTED WATERSHED MODELS

PROCESS-BASED, DISTRIBUTED WATERSHED MODELS. New generation Source waters and flowpaths Physically based. Objectives. Use distributed hydrologic modeling to improve understanding of the Hydrology (flowpaths?) water balance streamflow variability contaminant transport.

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PROCESS-BASED, DISTRIBUTED WATERSHED MODELS

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  1. PROCESS-BASED, DISTRIBUTED WATERSHED MODELS • New generation • Source waters and flowpaths • Physically based

  2. Objectives • Use distributed hydrologic modeling to improve understanding of the • Hydrology (flowpaths?) • water balance • streamflow variability • contaminant transport

  3. Objectives, continued • Test and validate model components and complete model against internal and spatially distributed measurements. • Isotopes are often ideal for cross-validating model results

  4. Objectives, continued • Evaluate the level of complexity needed to provide adequate characterization of streamflow at various scales. • Evaluate minimum data requirements • Evaluate minimum process-level information

  5. Objectives, continued • Quantify spatial heterogeneity of inputs (rainfall, topography, soils - where data exist) and relate this to heterogeneity in streamflow.

  6. Objectives, continued • Role of groundwater? • Fracture flow? • Back out as residual?

  7. Top Ten Reasons for Modeling • You don’t need data! • You don’t need to conduct fieldwork! • We’ll model it! • Synthesis • Diagnostic • Prognostic

  8. Distributed models incorporate the effects of topography through direct used of the digital elevation data during computation, along with process-level knowledge.

  9. Hydrological processes within a catchment are complex, involving: • Macropores • Heterogeneity • Fingering flow • Local pockets of saturation The general tendency of water to flow downhill is however subject to macroscale conceptualization

  10. MMS Modular Modeling SystemPRMS on Steroids • Conceptually, the framework is an integrated system of computer software designed to provide the modeling tools needed to support a broad range of model applications and model user skills. The framework supports the • application and analysis of existing models, • modification and enhancement of existing models for problem-specific applications, • research, devleopment, testing, and application of new models.

  11. TOP_PRMS PRMS National Weather Service - Hydro17 TOPMODEL

  12. PRECIPITATION-RUNOFF MODELING SYSTEM(PRMS) MODELING OVERVIEW & DAILY MODE COMPONENTS http://wwwbrr.cr.usgs.gov/projects/SW_precip_runoff/

  13. BASIC HYDROLOGIC MODEL Q = P - ET ± S Components Runoff Precip Met Vars Ground Water Soil Moisture Reservoirs Basin Chars Snow & Ice Water use Soil Moisture

  14. 3rd HRU DIMENSION

  15. Distributed Parameter Approach Hydrologic Response Units - HRUs HRU Delineation Based on: - Slope - Aspect - Elevation - Vegetation - Soil - Precip Distribution

  16. HRUs

  17. PRMS Parametersoriginal version

  18. Darcy’s Law Applied to Profile h i x depth p m0 mt Total head = h + x + p di/dt = K [(h + x + p) / x] I = x (mt -m0) h<<p [Green & Ampt]

  19. PRMS

  20. GROUND-WATER FLOW Qbase= RCB * Sgw Equation solved at 15 minute dt and pro rated to shorter dt as needed

  21. Relation of HRUs and Subsurface and GW Reservoirs Surface ( 6 hrus ) Subsurface ( 2 reservoirs ) Ground water (1 reservoir)

  22. PRMS • HANDLES DISTRIBUTED PRECIPITATION WELL • HANDLES INFILTRATION WELL • DOES NOT DO SO WELL WITH GROUNDWATER COMPONENT • SOLUTION: ADD TOPMODEL TO PRMS

  23. Terrain Based Runoff Generation Using TOPMODEL Beven, K., R. Lamb, P. Quinn, R. Romanowicz and J. Freer, (1995), "TOPMODEL," Chapter 18 in Computer Models of Watershed Hydrology, Edited by V. P. Singh, Water Resources Publications, Highlands Ranch, Colorado, p.627-668. “TOPMODEL is not a hydrological modeling package. It is rather a set of conceptual tools that can be used to reproduce the hydrological behaviour of catchments in a distributed or semi-distributed way, in particular the dynamics of surface or subsurface contributing areas.”

  24. TOPMODEL and GIS • Surface saturation and soil moisture deficits based on topography • Slope • Specific Catchment Area • Topographic Convergence • Partial contributing area concept • Saturation from below (Dunne) runoff generation mechanism

  25. Saturation in zones of convergent topography

  26. Topographic Index Topographic index is used to compute the depth to the water table, which in turn influences runoff generation: ln(A /tan b ) where ln is the natural logarithm, A is the area drained per unit contour or the specific area, and tan b is the slope

  27. Topographic Index • Regions of the landscape that drain large upstream areas or that are very flat give rise to high values of the index; • thus areas with the highest values are most likely to become saturated during a rain or snowmelt event and • thus are most likely to be areas that contribute surface runoff to the stream.

  28. Stream line Contour line Upslope contributing area a Numerical Evaluation with the D Algorithm Topographic Definition Specific catchment areaa is the upslope area per unit contour length [m2/m  m] Tarboton, D. G., (1997), "A New Method for the Determination of Flow Directions and Contributing Areas in Grid Digital Elevation Models," Water Resources Research, 33(2): 309-319.) (http://www.engineering.usu.edu/cee/faculty/dtarb/dinf.pdf)

  29. TOPMODEL assumptions • The dynamics of the saturated zone can be approximated by successive steady state representations. • The hydraulic gradient of the saturated zone can be approximated by the local surface topographic slope, tan. • The distribution of downslope transmissivity with depth is an exponential function of storage deficit or depth to the water table • To lateral transmissivity [m2/h] • S local storage deficit [m] • z local water table depth [m] • m a parameter [m] • f a scaling parameter [m-1]

  30. Topmodel - Assumptions D Dw S • The soil profile at each point has a finite capacity to transport water laterally downslope. e.g. or

  31. Topmodel D Dw S Specific catchment areaa [m2/m  m] (per unit coutour length) z

  32. Hydraulic conductivity (K) decreases with depth where z is local water table depth (m) f is a scaling parameter (m-1): shape of the decrease in K with depth

  33. GL4 CASE STUDY: OBJECTIVES • to test the applicability of the TOP_PRMS model for runoff simulation in seasonally snow-covered alpine catchments • to understand flowpaths determined by the TOP_PRMS model • to validate the flowpaths by comparing them with the flowpaths determined by tracer-mixing model

  34. RESAERCH SITE

  35. GIS WEASEL • Simplify the treatment of spatial information in modeling by providing tools (a set of ArcInfo 8 commands) to: (1) Delineate the basin from GRID DEM (2) Characterize stream flow direction, stream channels, and modeling response unit (MRU) (3) Parameterize input parameters for spatially distributed models such as TOPMODEL and TOP_PRMS model

  36. PROCEDURES FOR DELINEATION AND PARAMETERIZATION • DEM (10 m) was converted from TIN to GRID format using ArcInfo 8 commands • a pour-point coverage was generated using location information of gauging stations • DEM and the pour-point coverage were overlaid to delineate the basin • DEM slope and direction were re-classified to extract the drainage network • a base input parameter file and re-classified DEM were used to derive parameters needed for TOP_PRMS model

  37. DELINEATION FOR GREEN LAKE 4 • Delineated basin area: 220ha • Matches the real basin • Three HRU (MRU) delineated (one stream tributary one MRU)

  38. INPUT DATA • Measured discharge • Measured precipitation • Measured temperature • Measured solar radiation

  39. Calibration • Calibrate model with discharge in 1996 • Model calibrates internal processes and parameters to match discharge • Run model with climate parameters from modeling years • Calibration is key

  40. SIMULATED SNOWMELT VS. RUNOFFMartinelli

  41. Model Verification • Discharge is almost always used • Good idea or bad idea? Why?

  42. SENSITIVITY ANALYSIS AND PARAMETER CALIBRATION Sensitivity Analysis Parameter Calibration • Sensitivity controlled by optimization function of observed and modeled runoff • Sensitive parameters in snow module: snowmelt factor and sublimation rate • Sensitive parameters in topographic module: scaling factor and transmissivity • Rosenbrock optimization • Same optimization function as sensitivity analysis • Parameters in snow module control magnitude of modeled runoff • Parameters in topographic module control shape of rising and receding limbs • Improvement evaluated by modeling efficiency

  43. SENSITIVITY ANALYSIS AND PARAMETER CALIBRATION

  44. COMPARISON OF TOPOGRAPHIC PARAMETERS IN GLV WITH LOCH VALE

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