Hydrologic modeling to quantify watershed functioning and predict the sensitivity to change A discussion of ideas towards an integrated Water Sustainability and Climate Project. David G Tarboton Utah State University [email protected] www.engineering.usu.edu/dtarb. Outline. Some philosophy
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Hydrologic modeling to quantify watershed functioning and predict the sensitivity to change A discussion of ideas towards an integrated Water Sustainability and Climate Project
David G Tarboton
Utah State University
Advancing the capability for hydrologic prediction by developing models that take advantage of new information and process understanding enabled by new technology.
The questions that we ask as scientists shape everything that follows. They can lead us to see the world in new ways, or mundane ones. They can spur the development of new approaches, or the recyclingof established ones. They can focus our attention in useful directions, or leave us wandering aimlessly.
WATERS Network: that follows. How can we protect ecosystems and better manage and predict water availability and quality for future generations, given changes to the water cycle caused by human activities and climate trends?
Protect ecosystems and better manage and that follows. predict water availability and quality
Engineering: Integration of built environment water system
Social Sciences: People, institutions, and their water decisions
Hydrologic Sciences: Closing the water balance
WATERS Network science questions
How is fresh water availability changing, and how can we understand and predict these changes?
How can we engineer water infrastructure to be reliable, resilient and sustainable?
How will human behavior, policy design and institutional decisions affect and be affected by changes in water?
Resources needed to answer these questions and transform water science to address the Grand Challenges
Measurement of stores, fluxes, flow paths and residence times
Synoptic scale surveys of human behaviors and decisions
Water quality data for water throughout natural and built environment
Observatories, Experimental Facilities, Cyberinfrastructure
The Water Resources Inventory Area 1 (WRIA 1) Nooksack hydrologic model for decision support
Integrated model of Hydrologic, Water Management and Consumption processes at each “catchment”
Rainfall – Runoff Transformation Consumption processes at each “catchment”
Consumption processes at each “catchment”q1 ,, q2 , &yf
f & K
Soil derived parameters
Zone Code Polygon Layer
Depth weighted average
Soil Grid Layers
Joined to Polygon Layer
Exponential decrease with depth
Soil parameter look up by zone code
Table of Soil Hydraulic Properties – Clapp Hornberger 1978
Historic (pre-settlement) Consumption processes at each “catchment”
Vegetation derived parameters
Luce, C. H. and D. G. Tarboton, (2004), "The Application of Depletion Curves for Parameterization of Subgrid Variability of Snow," Hydrological Processes, 18: 1409-1422, DOI: 10.1002/hyp.1420.
The impact on streamflow of present land use Consumption processes at each “catchment”
Figure 4. Ratio of simulated existing streamflow with no water management to simulated historic streamflow, 30 year average over the years 1961-2005 at each node of the WRIA 1 surface water quantity model.
The impact on streamflow of present water management and use Consumption processes at each “catchment”
Figure 19. Ratio of simulated streamflow under existing conditions to simulated streamflow under existing conditions without water management.
Figure 25. Existing conditions simulation of user withdrawals from Deer Creek Drainage (Drainage 87)
Existing and Full Build Out scenario simulations of Lake Whatcom active storage.
Discharge from Lake Whatcom (Node 246).
Figure 35. Simulated Historic, Existing and Full Build Out Bertrand Creek Streamflow (ProjNodeID=515)
Figure 12. Flow duration curves for October and March in Bertrand Creek
Deer Creek cumulative water balance components simulated under Historic and Existing conditions without water management.
Streamflow at ProjnodeID=185, Drainage 109, location of Middle Fork Diversion
Streamflow at ProjnodeID=519, Drainage 163, location where Middle Fork Diversion discharges into Anderson Creek.
Ratio of mean streamflow simulated under Full Buildout conditions to mean streamflow simulated under existing conditions.
E=R natural and human system water systems
Energy limited upper bound
Water limited upper bound
Water LimitedA general framework for thinking about the overall water balance and change impactsS=P-Q-E P=Q+E
Dryness (Available Energy /Precip)
Following Budyko, M. I., (1974), Climate and Life, Academic, San Diego, 508 p.
Retention or Residence time natural and human system water systems
E = R : energy limited upper bound
E = P : water limited upper bound
Dryness (available energy /precip)
Budyko  partitioning of input water P into the evapotranspiration fraction, E/P, the residual of which is discharge Q. Dryness or aridity is quantified in terms of R/P. As dryness increases, the evapotranspiration fraction increases. For the same R/P the evaporative fraction is greater when retention is greater as retained water has more opportunity to evaporate or transpire.
Increasing variability in soil capacity or areas of imperviousness
Increasing Retention/Soil capacity
Increasing variability in P – both seasonally and with storm events
Precise observations of Precipitation, Runoff, Soil Moisture, Energy Balance, Water Storage required to discriminate among these hypotheses
Milly, P.C.D. and K.A. Dunne, 2002, Macroscale water fluxes 2: water and energy
supply control of their interannual variability, Water Resour. Res., 38(10).
Milly, P. C. D., (1994), "Climate, Soil Water Storage, and the Average Annual Water Balance," Water Resources Research, 30(7): 2143-2156.
B water problems
Development, Growth, Water Resources Management
Mountain Snow pack
Land Cover Land Use
Surface Salinity & Temperature
GSL Salt Load