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Physically-based Distributed Hydrologic Modeling

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Physically-based Distributed Hydrologic Modeling – PowerPoint PPT presentation

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Date added: 21 June 2018
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Title: Physically-based Distributed Hydrologic Modeling


1
Physically-based Distributed Hydrologic Modeling
2
Goal of Phys.-based Distrib. Hydrologic Modeling
  • To date we have learned about
  • Key forcings at land surface (precipitation/net
    radiation)
  • Physical processes at surface/subsurface
    (infiltration, soil moisture redistribution,
    evapotranspiration, groundwater flow, runoff,
    etc.)
  • Goal Develop physically-based model of
    hydrologic response across a watershed by tying
    together various processes across landscape.
  • In this context Distributed refers to variables
    being spatially-distributed in space.
  • So we aim to explicitly model how the hydrologic
    states/fluxes evolve in space and time throughout
    the watershed.
  • Note Because of complexity/nonlinearity of
    processes this modeling is necessarily done
    numerically (i.e. by building appropriate
    computer models coupling together hydrologic
    processes)

3
Representation of Dist. Hydrologic Units in
Space
Numerical simulations of catchment hydrologic
processes require a method for representing a
basin. Methods can be categorized as lumped
versus distributed modeling where the physical
processes are solved for each discrete unit.
Basin-Averaged Models (e.g. HEC-HMS)
Raster (Grid) Models (e.g. MIKE SHE)
Triangular Irregular Network Models
(e.g. tRIBS)
Will focus on this model as an example
4
tRIBS Distributed Model
TIN-based Real-time Integrated Basin Simulator
(tRIBS) is a fully-distributed model of coupled
hydrologic processes (Ivanov et al, Vivoni et al.)
  • Model Processes
  • Coupled vadose and saturated zones with dynamic
    water table.
  • Moisture infiltration waves.
  • Soil moisture redistribution.
  • Topography-driven lateral fluxes in vadose and
    groundwater.
  • Radiation and energy balance.
  • Evaporation and Transpiration.
  • Hydrologic and hydraulic routing.

Radiation
Key point You now know about all of these
processes a distributed model simply ties them
all together.
5
Process Representation Surface Processes
Land-Atmosphere Interactions
  • Coupled Energy and Hydrology Processes on Complex
    Terrain
  • Radiation Incoming short-wave and long-wave,
    outgoing long-wave radiation (including effects
    of terrain).
  • Vegetation Canopy interception, drainage,
    throughfall and transpiration using vegetation
    functional type.
  • Energy Balance Net radiation, ground heat,
    sensible heat and latent heat fluxes.
  • Evapotranspiration Soil-moisture controlled
    bare soil evaporation and canopy transpiration in
    root zone.
  • Unsaturated Zone Dynamics Soil moisture
    balance, infiltration, redistribution

Vegetation
3D Complex Topography
Soil
Radiation Balance
Surface Energy Balance
Aquifer
6
Process Representation Subsurface Processes
Uses a simplified 2D unconfined aquifer model
which allows moisture recharge in shallow aquifer
to be redistributed.
  • Shallow Groundwater
  • Space/time variable groundwater table position.
  • Single and multiple direction GW flow to
    downstream neighbors.
  • Coupled to unsaturated zone to enable moisture
    mass balance (recharge).
  • Bounded by a uniform or spatially-variable
    bedrock surface (impermeable bottom boundary).

Variable, dynamic water table field (plan view)
head gradients drive flow
7
Process Representation Unsat.-Sat. Dynamics
Runoff is generated via multiple mechanisms
depending on the interactions of infiltration
fronts and the water table.
  • Runoff Generation
  • Interaction of rainfall, infiltration capacity,
    actual infiltration and lateral flows lead to
    various runoff types.
  • Various runoff types occur at the same time in
    different basin parts.
  • Various runoff types can occur in single element
    as a function of state.
  • Infiltration-excess (Hortonian) Runoff.
  • Saturation-excess (Dunne) Runoff.
  • Perched Subsurface Runoff.
  • Groundwater Runoff.

Example Model output for saturation-excess
runoff occurrence
8
Atmospheric Forcing
Primary reason for using distributed models is to
take advantage of new distributed atmospheric
forcing datasets (e.g. precipitation, radiation,
etc).
NEXRAD MOSAIC PRECIP.
SATELLITE ESTIMATES OF LONGWAVE RAD.
SHORTWAVE RAD.
9
tRIBS Model Output
  • tRIBS provides output at the scale of each
    individual node in the basin, for channel nodes
    along the network, and as maps of distributed
    variables (at a point in time or integrated over
    time).
  • Time Series of Node Behavior Unsaturated and
    Saturated Node Dynamics, Hydrologic and Energy
    Fluxes and State Variables.
  • Basin Outlet and Interior Channel Nodes Runoff
    Depth, Discharge, Stream Velocity, Partitioned
    Hydrographs.
  • Dynamic Distributed Maps Groundwater dynamics,
    Surface Runoff Generation Mechanisms, Soil
    Moisture, Evapotranspiration, Rainfall,
    Interception, Unsaturated Zone Dynamics, Energy
    and Radiation.
  • Integrated Distributed Maps Percent Runoff
    Mechanisms, Saturation Occurrence, Evaporation
    Fraction, Soil Moisture.
  • Time Series of Basin Averaged Properties
    Rainfall, Saturated Area, Evapotranspiration,
    Soil Moisture.

NOTE Provides much more information than a
lumped model!
10
Illustrative Example Peacheater Creek
Two-year precipitation record
11
Parameter Definitions for Basin
(silt loam)
(mixed forest)
(everg. forest)
(decid. forest)
(silty clay)
(crops)
(clay/ urban)
(urban)
Note Spatially varying inputs in soil/vegetation
-- impacts spatial variability in hydrologic
response
12
Streamflow Response (Storm at Hour 11800)
13
Groundwater Before/after
14
Soil Moisture Before/after
15
Surface Energy Balance
16
Summary
  • Distributed hydrologic modeling provides an
    integrated framework for taking into account
    hydrologic processes occurring within the basin
    (surface energy balance, flow partitioning, etc.)
  • Allows for not only simulating design flows/flood
    forecasts (i.e. as done using UH-method), but for
    things like assessing spatial response to inputs,
    hydrologic impacts resulting from urbanization of
    watersheds, assessing climatology of hydrologic
    states, etc.
  • Takes advantage of many new distributed
    forcing/parameter databases obtained via remote
    sensing (lumped models do not take advantage of
    spatially distributed inputs)
  • Is computationally demanding (e.g. compared to
    UH) and therefore whether it should be used is
    largely application dependent
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