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

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 “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) 125 th 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?

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

Landslides in Stillman Creek Drainage, upper Chehalis River Basin, WA, December, 2007 Visual courtesy Steve Ringman, The Seattle Times

1. Hydrologic effects of land use and land cover change

Background: Cropland expansion Ramankutty and Foley, Global Biogeochem. Cycles, 1999 Percentage of global land area: 3 14

Clearcutting in the Pacfic Northwest Visuals from Osborne (2001) and Sightline Institute

Source: Van Shaar et al, Hydrological Processes, 2002 How well do we predict the hydrologic signature of land cover change?

Source: Van Shaar et al, Hydrological Processes, 2002

2. Hydrologic sensitivity to climate change

The role of changing climate, source: Mote et al (2005)

Tmin at selected Puget Sound basin stations,

Tmax at selected Puget Sound basin stations,

from Seager et al, Science, 2007

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

Magnitude and Consistency of Model-Projected Changes in Annual Runoff by Water Resources Region, Median change in annual runoff from 24 numerical experiments (color scale) and fraction of 24 experiments producing common direction of change (inset numerical values). +25% +10% +5% +2% -2% -5% -10% -25% Decrease Increase (After Milly, P.C.D., K.A. Dunne, A.V. Vecchia, Global pattern of trends in streamflow and water availability in a changing climate, Nature, 438, , 2005.) 96% 75% 67% 62% 87% 71% 67% 62% 58% 67% 62% 58% 67% 100%

Dooge (1992; 1999): For temperature, it’s more convenient to think in terms of sensitivity (v. elasticity) where Ψ P is elasticity of runoff with respect to precipitation

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

ModelPrecipitation -Elasticity Temp- sensitivity (Tmin & Tmax ) %/ 0 C Temp- sensitivity ( Tmax) %/ 0 C Lees Ferry (MACF) VIC NOAH SAC Hoerling 1.77 ~ ~ ? Summary of precipitation elasticities and temperatures sensitivities for Colorado River at Lees Ferry for VIC, NOAH, and SAC models

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

Spatial distribution of precipitation elasticities Censored spatial distribution of annual runoff

Composite seasonal water cycle, by quartile of the runoff elasticity distribution

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

Censored spatial distribution of annual runoff Spatial distribution of temperature sensitivities (Tmin fixed)

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

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

Spatial distribution of temperature sensitivities (equal changes in Tmin and Tmax) Censored spatial distribution of annual runoff

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

3. Hydrologic effects of water management structures

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

Global Water System Project IGBP – IHDP – WCRP - Diversitas Human modification of hydrological systems Columbia River at the Dalles, OR

What protocols do we have to evaluate our ability to predict hydrologic change? Klemes (Hyd Sci. J., 1986) argues for testing based on a)split sample (SS), at the same site b)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 c)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) d)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

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)

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. Conclusions