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

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PROCESS-BASED, DISTRIBUTED WATERSHED MODELS


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Presentation Transcript
slide1

PROCESS-BASED,

DISTRIBUTED

WATERSHED MODELS

  • New generation
  • Source waters and flowpaths
  • Physically based
objectives
Objectives
  • Use distributed hydrologic modeling to improve understanding of the hydrology, water balance and streamflow variability.
    • Test and validate model components and complete model against internal and spatially distributed measurements.
    • Evaluate the level of complexity needed to provide adequate characterization of streamflow at various scales.
    • Quantify spatial heterogeneity of inputs (rainfall, topography, soils - where data exist) and relate this to heterogeneity in streamflow.
    • Role of groundwater? Fracture flow?
slide3

Distributed models incorporate the effects of topography through direct used of

the digital elevation data during computation, along with process-level knowledge.

hydrological processes within a catchment are complex involving
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

slide6

TOP_PRMS

PRMS

National Weather Service - Hydro17

TOPMODEL

slide7

PRECIPITATION-RUNOFF MODELING SYSTEM(PRMS)

MODELING OVERVIEW

&

DAILY MODE COMPONENTS

http://wwwbrr.cr.usgs.gov/projects/SW_precip_runoff/

slide9

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

slide10

Distributed Parameter Approach

Hydrologic Response Units - HRUs

HRU Delineation Based on:

- Slope - Aspect

- Elevation - Vegetation

- Soil - Precip Distribution

slide14

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]

slide16

Relation of HRUs and Subsurface and GW Reservoirs

Surface ( 6 hrus )

Subsurface ( 2 reservoirs )

Ground water (1 reservoir)

slide17
PRMS
  • HANDLES DISTRIBUTED PRECIPITATION WELL
  • HANDLES INFILTRATION WELL
  • DOES NOT DO SO WELL WITH GROUNDWATER COMPONENT
  • SOLUTION: ADD TOPMODEL TO PRMS
terrain based runoff generation using topmodel

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.”

topmodel and gis
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
slide21

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

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.

slide22

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)

topmodel assumptions
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]
slide24

Topmodel - Assumptions

D

Dw

S

  • The soil profile at each point has a finite capacity to transport water laterally downslope.

e.g.

or

slide25

Topmodel

D

Dw

S

Specific catchment areaa [m2/m  m]

(per unit coutour length)

z

slide26

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

slide28

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
slide30

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

slide31

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
slide32

DELINEATION FOR GREEN LAKE 4

  • Delineated basin area: 220ha
  • Matches the real basin
  • Three HRU (MRU) delineated (one stream tributary one MRU)
slide33

INPUT DATA

  • Measured discharge
  • Measured precipitation
  • Measured temperature
  • Measured solar radiation
slide35

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
slide39

PROBLEM ON RUNOFF SIMULATION

  • Runoff peaks in May and June failed to be captured by the model
  • The modeled runoff tells us that a large amount of snowmelt was infiltrated into soil to increase soil water storage
  • However, the reality is that there were runoff peaks in May and June as observed
  • It is hypothesized that a large amount of the snowmelt produced in May and June may contribute to the stream flow via overland and topsoil flowpaths due to impermeable barrier of frozen soils and basal ice
summary and conclusions
Summary and Conclusions
  • Modeling system centered on TOPMODEL for representation of spatially distributed water balance based upon topography and GIS data (vegetation and soils).
  • Capability to automatically set up and run at different model element scales.
  • Encouraged by small scale calibration, though physical interpretation of calibrated parameters is problematic.
  • Large scale water balance problem due to difficulty relating precipitation to topography had to be resolved using rather empirical adjustment method.
  • Results provide hourly simulations of streamflow over the entire watershed.
modflow
MODFLOW
  • THE IDEAL SITUATION FOR GROUNDWATER TYPES WOULD BE TO COMBINE PRMS WITH MODFLOW
  • MODFLOW-PRMS CONNECTION IS BEING DONE TODAY
  • BETA VERSIONS NOT YET AVAILABLE, BUT SOON
slide43

WARNING: TAKE ALL

MODELS WITH A GRAIN OF SALT!

DON’T HAVE TOO MUCH

CONFIDENCE IN MODELS!

slide44

REFERENCES

  • Leavesley, G.H., Lichty, R.W., Troutman, B.M., and Saindon, L.G., 1983, Precipitation-runoff modeling system--Users manual: U.S. Geological Survey Water-Resources Investigations Report 83-4238, 207 p.
  • Leavesley, G.H., Restrepo, P.J., Markstrom, S.L., Dixon, M., and Stannard, L.G., 1996, The modular modeling system (MMS)--User's manual: U.S. Geological Survey Open-File Report 96-151, 142 p.
  • Mastin, M.C., and Vaccaro, J.J., in press, Watershed models for decision support in the Yakima River Basin, Washington: U.S. Geological Survey Open-File Report..
  • Ryan, Thomas, 1996, Global climate change response program--Development and application of a physically based distributed parameter rainfall runoff model in the Gunnison river basin: United States Department of Interior, Bureau of Reclamation, 64 p.