Physically-based Distributed Hydrologic Modeling Goal of Phys.-based Distrib. Hy
Zimney, John, News Director has reference to this Academic Journal, PHwiki organized this Journal Physically-based Distributed Hydrologic Modeling Goal of Phys.-based Distrib. Hydrologic Modeling To date we have learned about: Key as long as cings at l in addition to 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 l in addition to scape. 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 in addition to 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) Representation of Dist. Hydrologic Units in Space Numerical simulations of catchment hydrologic processes require a method as long as representing a basin. Methods can be categorized as lumped versus distributed modeling where the physical processes are solved as long as 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
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Model Processes Coupled vadose in addition to saturated zones with dynamic water table. Moisture infiltration waves. Soil moisture redistribution. Topography-driven lateral fluxes in vadose in addition to groundwater. Radiation in addition to energy balance. Evaporation in addition to Transpiration. Hydrologic in addition to hydraulic routing. TIN-based Real-time Integrated Basin Simulator (tRIBS) is a fully-distributed model of coupled hydrologic processes (Ivanov et al, Vivoni et al.) tRIBS Distributed Model Radiation Key point: You now know about all of these processes; a distributed model simply ties them all together. L in addition to -Atmosphere Interactions Vegetation Soil Aquifer 3D Complex Topography Coupled Energy in addition to Hydrology Processes on Complex Terrain Radiation: Incoming short-wave in addition to long-wave, outgoing long-wave radiation (including effects of terrain). Vegetation: Canopy interception, drainage, throughfall in addition to transpiration using vegetation functional type. Energy Balance: Net radiation, ground heat, sensible heat in addition to latent heat fluxes. Evapotranspiration: Soil-moisture controlled bare soil evaporation in addition to canopy transpiration in root zone. Unsaturated Zone Dynamics: Soil moisture balance, infiltration, redistribution Radiation Balance Process Representation: Surface Processes Surface Energy Balance 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 in addition to multiple direction GW flow to downstream neighbors. Coupled to unsaturated zone to enable moisture mass balance (recharge). Bounded by a uni as long as m or spatially-variable bedrock surface (impermeable bottom boundary). Variable, dynamic water table field (plan view) head gradients drive flow
Process Representation: Unsat.-Sat. Dynamics Runoff is generated via multiple mechanisms depending on the interactions of infiltration fronts in addition to the water table. Runoff Generation Interaction of rainfall, infiltration capacity, actual infiltration in addition to 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 as long as saturation-excess runoff occurrence Atmospheric Forcing SATELLITE ESTIMATES OF: LONGWAVE RAD. SHORTWAVE RAD. Primary reason as long as using distributed models is to take advantage of new distributed atmospheric as long as cing datasets (e.g. precipitation, radiation, etc). NEXRAD MOSAIC PRECIP. tRIBS Model Output tRIBS provides output at the scale of each individual node in the basin, as long as channel nodes along the network, in addition to as maps of distributed variables (at a point in time or integrated over time). Time Series of Node Behavior: Unsaturated in addition to Saturated Node Dynamics, Hydrologic in addition to Energy Fluxes in addition to State Variables. Basin Outlet in addition to 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 in addition to 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 in as long as mation than a lumped model!
Illustrative Example: Peacheater Creek Two-year precipitation record Parameter Definitions as long as Basin Note: Spatially varying inputs in soil/vegetation – impacts spatial variability in hydrologic response (urban) (crops) (decid. as long as est) (everg. as long as est) (mixed as long as est) (clay/ urban) (silty clay) (silt loam) Streamflow Response (Storm at ~Hour 11800)
Groundwater: Be as long as e/after Soil Moisture: Be as long as e/after Surface Energy Balance
Summary Distributed hydrologic modeling provides an integrated framework as long as taking into account hydrologic processes occurring within the basin (surface energy balance, flow partitioning, etc.) Allows as long as not only simulating design flows/flood as long as ecasts (i.e. as done using UH-method), but as long as 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 as long as cing/parameter databases obtained via remote sensing (lumped models do not take advantage of spatially distributed inputs) Is computationally dem in addition to ing (e.g. compared to UH) in addition to there as long as e whether it should be used is largely application dependent
Zimney, John News Director
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