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Some thoughts on predicting hydrologic futures: The role of model sensitivity. Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington. Berkekey Catchment Science Symposium 2008 UC Berkeley December 14, 2008. Outline of this talk.

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slide1
Some thoughts on predicting hydrologic futures: The role of model sensitivity

Dennis P. Lettenmaier

Department of Civil and Environmental Engineering

University of Washington

Berkekey Catchment Science Symposium 2008

UC Berkeley

December 14, 2008

outline of this talk
Outline of this talk
  • The role of hydrology in Earth system science
  • What are the grand challenges in hydrology?
  • Understanding hydrologic change examples:
    • Land cover and land use change
    • Climate change
    • Water management
  • Do we have a framework for evaluating our ability to predict change?
the role of hydrology in earth system science
The role of hydrology in Earth system science

“Where is the water, where is it going and coming from and at what rate, and what controls its movement and that of the constituents that move with it?”

what are the grand challenges in hydrology
From Science (2006) 125th Anniversary issue (of eight in Environmental Sciences): Hydrologic forecasting – floods, droughts, and contamination

From the CUAHSI Science and Implementation Plan (2007): … a more comprehensive and … systematic understanding of continental water dynamics …

From the USGCRP Water Cycle Study Group, 2001 (Hornberger Report):[understanding] the causes of water cycle variations on global and regional scales, to what extent [they] are predictable, [and] how … water and nutrient cycles [are] linked?

What are the “grand challenges” in hydrology?
slide5
Important problems all, but I will argue instead (in addition) that understanding hydrologic change should rise to the level of a grand challenge to the community.
basic premise
Humans have greatly affected the land surface water cycle through

Land cover change

Climate change

Water management

While climate change has received the most attention, other change agents may well be more significant

Basic premise
slide7

Landslides in Stillman Creek Drainage, upper Chehalis River Basin, WA, December, 2007

Visual courtesy Steve Ringman, The Seattle Times

background cropland expansion
Background: Cropland expansion

Percentage of global land area:

3

14

Ramankutty and Foley, Global Biogeochem. Cycles, 1999

slide10

Clearcutting in the Pacfic Northwest

Visuals from Osborne (2001) and Sightline Institute

slide11

How well do we predict the hydrologic signature of land cover change?

Source: Van Shaar et al, Hydrological Processes, 2002

slide25
Postmortem: Christensen and Lettenmaier (HESSD, 2007) – multimodel ensemble analysis with 11 IPCC AR4 models (downscaled as in C&L, 2004)
slide26

Magnitude and Consistency of Model-Projected Changesin Annual Runoff by Water Resources Region, 2041-2060

Median change in annual runoff from 24 numerical experiments (color scale)

and fraction of 24 experiments producing common direction of change (insetnumerical values).

+25%

58%

+10%

Increase

67%

62%

+5%

58%

87%

96%

+2%

62%

62%

71%

87%

-2%

75%

67%

67%

67%

-5%

100%

Decrease

-10%

-25%

(After Milly, P.C.D., K.A. Dunne, A.V. Vecchia, Global pattern of trends in streamflow andwater availability in a changing climate, Nature, 438, 347-350, 2005.)

slide27
Dooge (1992; 1999):

where ΨP is elasticity of runoff with respect to precipitation

For temperature, it’s more convenient to think in terms of sensitivity (v. elasticity)

slide28

Inferred runoff elasticities wrt precipitation for major Colorado River tributaries, using method of Sankarasubramanian and Vogel (2001)

Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography

slide29

Summary of precipitation elasticities and temperatures sensitivities for Colorado River at Lees Ferry for VIC, NOAH, and SAC models

slide30

VIC Precipitation elasticity histograms, all grid cells and 25% of grid cells producing most (~73%) of runoff

slide31

Spatial distribution of precipitation elasticities

Censored spatial distribution of annual runoff

slide33

Temperature sensitivity (Tmin fixed) histograms, all grid cells and 25% of grid cells producing most (~73%) of runoff

slide34

Spatial distribution of temperature sensitivities (Tmin fixed)

Censored spatial distribution of annual runoff

slide35

Composite seasonal water cycle, by quartile of the temperature sensitivity (fixed Tmin) distribution

slide36

Temperature sensitivity (equal change in Tmin and Tmax) histograms, all grid cells and 25% of grid cells producing most (~73%) of runoff

slide37

Spatial distribution of temperature sensitivities (equal changes in Tmin and Tmax)

Censored spatial distribution of annual runoff

slide38

Composite seasonal water cycle, by quartile of the temperature sensitivity (equal change in Tmin and Tmax) distribution

slide40

Global Reservoir Database

Location (lat./lon.), Storage capacity, Area of water surface,

Purpose of dam, Year of construction, …

13,382dams,

Visual courtesy of Kuni Takeuchi

slide41

Global Water System Project

IGBP – IHDP – WCRP - Diversitas

Human modification

of hydrological systems

Columbia River at the Dalles, OR

slide42

What protocols do we have to evaluate our ability to predict hydrologic change?

  • Klemes (Hyd Sci. J., 1986) argues for testing based on
  • split sample (SS), at the same site
  • Differential split sample (DSS), where model is calibrated to “pre” condition (e.g., pre-cutting), appropriate model characteristics (e.g., change in LAI) are adjusted, and model predictions are tested against “post” data
  • Proxy basin test (PB), where model is transferred from one basin, and applied to the PB without direct calibation there (but using parameter transfer algorithms that may include other basins)
  • Proxy basin differential split sample (PC-DSS), transfer from one (or more) basins and from pre to post period.

Refsgaard and Knudsen (WRR, 1996) apply this construct

slide43

Some challenges

  • The framework is a bit specific to streamflow prediction (and calibration protocols, etc.)
  • Signal to noise issues often preclude evaluation of model performance over the relatively short time periods for which data (especially DSS variations) exist, yet modest long-term changes can have substantial practical effects (e.g., the Lake Mead example)
  • Data problems (especially the case in the DSS variants), and confounding of nonstationarity with the SS protocol (can be addressed by sample design variations, e.g., “shuffled deck” rather than split sample
  • Where good DSS data sets are available (e.g., H.J. Andrews), there often is a mismatch in spatial scale, and the magnitude of the disturbance signature
  • Opportunistic DSS data sets often don’t include observations of key variables, observation periods too short, etc (e.g., Entiat Experimental Basins)
slide44

Conclusions

  • We need to understand hydrologic sensitivities – to vegetation and climate change – better. There is a compelling motivation to do so both both on a scientific basis, and to address societal needs.
  • The uncertainties in predicting sensitivities to processes driven by temperature and/or evaporative demand changes seem to be greater than those related to precipitation change, even though in the climate world, prediction of precipitaiton change is generally considered more difficult than temperature
  • Although some hydrological consequences of water management are essentially deterministic, others are not, and we do not have a unified approach to addressing these issues – the history is much more one of case studies. Until and unless this can be done, development of unified approaches to predicting hydrologic change associated with water management will be impeded.