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Integrated Modeling of Regional Basins: Thirteen Years of Hard Lesson Learned Mark A. Ross, Patrick D. Tara, and Jeffrey S. Geurink University of South.

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Presentation on theme: "Integrated Modeling of Regional Basins: Thirteen Years of Hard Lesson Learned Mark A. Ross, Patrick D. Tara, and Jeffrey S. Geurink University of South."— Presentation transcript:

1 Integrated Modeling of Regional Basins: Thirteen Years of Hard Lesson Learned Mark A. Ross, Patrick D. Tara, and Jeffrey S. Geurink University of South Florida

2 Integrated Model: Coupled Comprehensive Surface- Groundwater Model (Specifically: Experience with coupled HSPF-MODFLOW model known as FHM, ISGW, or IHM)

3 PrecipitationTranspiration Evaporation Baseflow Runoff Water Table Infiltration & Percolation Interflow Leakage Interception Impervious Lens Surface water storages & stream flows Groundwater Flow Aquitard (Confining Unit) Confined or Artesian Aquifer Unconfined Aquifer Evaporation Bays, Gulf, and Oceans Hydrologic Cycle Process Coupling 1. DTWT 2. Hydr. heads (streams, wetlands & lakes) 3. Base flow 4. SW/GW ET 5. Irrigation fluxes 6. Variable SY (vadose moist.) FHM (HSPF/MODFLOW) Integration Pathways

4 FHM Chronology  1989 FHM development  1993 FHM ver 1.2 used in mine reclamation  1995 ISGW proprietary version used for public water supply investigations, FHM District-scale application  1995-1997 FHM peer-reviewed, adapted for USFWS water rights investigations  1995-1998 period of W-C Fl water wars, numerous models  1997-2000 several FHM version updates for regional W-C Fl regional investigations, ISGW peer reviewed  2001-2002 major re-write of FHM/ISGW->IHM (Interra, Aquaterra, USF)

5 Early Problems (Limitations)  Computers (286s)  Model components (HSPF & MODFLOW)  Data utilities (GIS, database programs)  Digital data (no digital quads)  Client perspectives/interest (“recharge generator”)  User acceptance (“too much time & money”) Consequence: Limited Discretization

6 R 18 W R 17 W R 16 W T 11 N Lake Havasu River 95 N Bill Williams National Wildlife Refuge

7 Cibola National Wildlife Refuge

8 T 16 N T 15 N R 16 ER 17 E N Las Vegas National Wildlife Refuge

9 Rattlesnake Creek Study

10 Model Simulation Results Rattlesnake Creek

11 USFWS Applications Lessons Learned  Need does not justify model where there is no data  Streamflow separation (runoff/baseflow) very important to do but problematic  Component pre-calibration very important  Size and discretization barriers remained  Need much better data utilities

12 Mid 90s “Water Wars” West-Central Florida  Many model applications, similar stream flow performance & gross ET, wildly varying resultant models (recharge), conclusions  Wide variability in model parameterization resulted from inadequate data, understanding and characterization of processes  Need for detailed basin-scale study, tie down internal fluxes and storages

13 le Creek Basin Outline Distgen.shp Saddle Creek Study (Data Collection, Far-field and Near- field Models) N EW S

14 Saddle Creek Gauging Stations

15 Station 17 Calibration

16 Saddle Creek Study Lessons Learned  Extensive basin-scale data collection helped refine model calibration and resultant internal fluxes  Real important to characterize time/space scale of rainfall  Needed to understand the mechanism of runoff, especially role of variable saturated areas (VSAs)  Variable specific yield (SY) very important process

17 SWFWMD Southern District Model HSPF, MODFLOW pre-calibration, 1 st phase of integrated model

18 SWFWMD Southern District Model Lessons Learned  Importance of including all hydrography explicitly  Strong parameterization and model performance constraints by DTWT resulted in greatly improved calibration (streamflow and aquifer behavior)  Indicated much higher GW ET fraction in shallow watertable settings than previously considered – resulting in model concept changes  Importance of irrigation fluxes and deep aquifer discharges zones

19 Alafia Subbasins

20 Alafia Model Lesson  Need to explicitly characterize connected and unconnected hydrography for each basin  Depart from basin calibration, move to land use calibration

21 Alafia Micro-Scale Field Study Preliminary Results  Runoff dominated by saturation excess (VSAs)  Air entrapment plays a strong role  Water table fluctuations are very rapid  Baseflow timescale may be controlled by ET timescale not water table drainage  SY is highly variable (.2 – 2 m) controlling water table fluctuation  Vadose zone moisture maintenance pronounced, thus significant GW ET (watertable depths < 2 m)

22 New IHM Model Developments  New code integration structure, HSPF and MODFLOW run concurrently, greatly enhanced capability and enhanced run speed  Integration and all other timesteps completely user defined  Landforms explicitly modeled (more distributed parameterization)

23 Old Model Structure

24 New Model Structure

25 TBW Connected & Unconnected Hydrography DICRETIZATION MODFLOW: 20,000 grids (1/4 mi) 150,000 river reaches 3 aquifer layers HSPF: 172 non-connected reaches 172 storage attenuation 73 routing reaches 172 basins 5 landform categories 320,000 landuse polygons

26 Three-Layer Soil Moisture Model

27 Important Spatial & Temporal Scales ( Shallow Aquifer Coastal Plain Systems) Rainfall Runoff flow plain Infiltration 1 km 2 10 2 m 5-15 min. ET: Surface Vadoze Water table 10 2 m Horizontal.1 – 3 m Vertical 3 m V hourly daily Daily Rchg: Vadose zone Surficial Confined 0.1-2 m 10 2 mH, 1 mV, 10 3 mH, 10-10 2 mV 1-24 hrs. Wkly Other: Stream base flow GW Pumping sens. Landuse change sens. 10 3 m Vars. daily – season daily 1-5 years

28 Overall Conclusions & Recommendations  Understand the hydrologic processes and water budget magnitudes before beginning  Ensure adequate data to support model  Commit to the data pre-analysis  Understand the limitations and long-term commitments  Attention to internal fluxes and storages will ensure fully constrained, unique solution

29 Integrated Model Commitments  Enormous data requirements  Complete surface water dataset  Complete groundwater dataset  Data pertaining to the integration  Different timescales and space scales  Considerable data analysis prior to calibration  More difficulty in calibration  Users must possess both SW and GW expertise

30 Root Capillary Zone

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