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