HYDROLOGY IN AN ERA OF GLOBAL CHANGE

Robert E. Horton Lecture

Dennis P. Lettenmaier

Department of Civil and Environmental EngineeringUniversity of Washington

American Meteorological Society Annual Meeting 22nd Conference on Hydrology

New Orleans

January 22, 2008

Hulin Gao Amanda Tan Kristian Mickelson Shrad Shukla Mergia Sonessa Chunmei Zhu Lan Cuo

UW LAND SURFACE HYDROLOGY RESEARCH GROUP 2008

Fransisco Munoz Alan HamletDennis Lettenmaier

Andrew Wood John Yearsley Nathalie VoisinTed Bohn

Tazebe Beyenne Quihong Tang Ben Livneh Kostas Andreadis Xiaogong Shi Elizabeth Clark

With thanks to the University of Washington Land Surface Hydrology Group

And especially:

Kostas Andreadis (UW)Tazebe Beyenne (UW)Elizabeth Clark (UW)Lan Cuo (UW)Mariza Costa-Cabral (Hydrology Futures, Seattle)Ingjerd Haddeland (Norwegian Water Resources and

Energy Directorate)Hugo Hidalgo (Scripps Institution of Oceanography)Ben Livneh (UW)Ramiro Saurral and Vicente Barros (University of

Buenos Aires)Amanda Tan (UW)

Robert E. Horton (1875-1945)• Published 100-200 papers (no known bibliography)

• Best known for 1933 Trans AGU paper “The role of infiltration in the hydrologic cycle”

• However, much of his early work (e.g., MWR, 1905) dealt with snow hydrology

• 24 papers appeared in MWR, earliest in May 1905, last in Apr. 1934

• Last papers appeared shortly before his death, e.g. “Erosional development of streams” (Trans GSA, 1945)

• Comments in Science (Dec. 10, 1937) “Hydrology research”: All hydrologic phenomena are in reality physical phenomena and are governed by the fundamental laws of physics. Many otherwise excellent hydrologic researches have suffered from lack of adequate consideration of the physical processes involved and from the failure to use mathematical methods.

Water balance of the continental U.S., from “Hydrologic interrelations between lands and oceans,” Robert E. Horton, Trans AGU, 1943.

Aggregated Maurer et al. (2002) data vs Horton (1943)

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?

Important problems all, but I will argue instead (in addition) that understanding hydrologic sensitivities to global change should rise to the level of a grand challenge to the community.

In an era of global change …

• What are the impacts of land use and land cover change on river basin hydrology?

• What is the climatic sensitivity of runoff?

• What are the impacts of water management on the water cycle?

1. Land cover/land use change effects

Global cropland expansion, 1700-1992 (from Ramankutty and Foley, Global Biogeochem. Cycles, 1999)

Do we understand the sensitivities?

Case study 1: Vegetation and climate change effects on streamflow in the Uruguay River basin

Forest/Woodland

Shrubland/grassland

Cropland

1990s land cover (U MD) Global Potential Vegetation (Ramankutty and Foley)

Uruguay River basin land cover change – potential vegetation vs 1990s

Simulated and observed streamflows, Uruguay River at Concordia, Uruguay – calibration (1995-99) and verification (1990-94).

Visual courtesy Ramiro Saurral and Vicente Barros, University of Buenos Aires

Simulated and observed mean monthly flows at Concordia, 1990-99 for ~1990 land cover, and sensitivity to land cover change (forest type 7; grassland type 10)

Visual courtesy Vicente Barros and Ramiro Saurral, University of Buenos Aires

Predicted and observed Concordia discharge, decade of 1960s (upper) and 1990s (lower), both simulations using 1990s vegetation, and consistent observing network for two decades.

Visual courtesy Vicente Barros and Ramiro Saurral, University of Buenos Aires

Case study 2: Land cover change in the Mekong River basin

1946

AREA OF DETAIL

1984

The broad low land along the Mun River was drained for more irrigated rice. The interfluves of tributa- ries of the Mun and Chi were converted to (bunded) rainfed rice.

From: Fukui et al., Global Environ. Res. 3 (2), 2000.

EXPANSION OF

RICE PADDIES

100 km

10 km

Yasothon(Chi river)

Ubon

Rasi Salai(Mun river)

Chiang Saen

VientianeMukdahan

Pakse

Stung Treng (S)

Outlet (O)

Phnom Penh (P)

Mun-Chisub-basin

Junction

downstream distance Ubon

Chi

MunChi

Mun

Yasothon (Chi)

RasiSalai (Mun)

dryseason

wetseason

dryseason

wetseason

downstream distance

ChiangSaen Vient. Muk. S P O

Mainstem

•In the dry season (Nov-Apr), cultivation is limited, and ET from cropland is far less than from forest. The simulated change from forest to cropland agrees with observations for 1962-2000 (~120% increase).

•In the wet season (May-Oct), simulated evapo-transpiration from bunded rice paddies is large but does not quite reach that of forest.

Predicted streamflow trends

OBSERVED STREAMFLOW TRENDS:Percent Change in Monthly Flows Per Year in 1962-2000

(based on the Mann-Kendall test for trends)

-4

-3

-2

-1

0

1

2

3

4

jan feb mar apr may jun jul aug sep oct nov dec

Tre

nd

Slo

pe

as %

of

Mo

nth

's A

vera

ge

Chiang SaenVientiane minus Chiang SaenMukdahan minus VientianePakse minus MukdahanUbonYasothonStung Treng minus Pakse

Streamflows from Northeast Thailand show fast-rising trends in the dry season months (Winter).

Chi River (Yasothon): A ~3% increase per year in dry-season streamflow leads to a ~120% increase (more than a doubling) in the 40 years from 1962 to 2000.

Streamflows from Laos show decreasing trends in the dry season months (Winter).

Case study 3: Land cover change in an urbanizing catchment, Mercer Creek, WA

1882 2002

Mercer Creek (~31.1 km2) land cover, 1882 and 2002

Mercer Creek annual flows 1955-2006, and double mass curve

2. What is the climatic elasticity of runoff?

Replotted from Seager et al., Science, 2007

19-model GCM average, Colorado River basin, annual values 2001-2100

Dooge (1992; 1999):

where

and

(Budyko curve)

Special cases:

a) AE = constant: ΨP = P/Q (inverse of runoff ratio)

b) P/PE large (e.g., tundra): ΨP = 1

c) P/PE small (desert): depends on Φ’(0) (but ΨP ~ 3 for some forms)

Precipitation sensitivity is straightforward

Evapotranspiration, however, depends on net radiation and vapor pressure deficit (among other variables), whereas (air) temperature is the more commonly observed variable

Air temperature in turn, affects (or is affected by):

• downward solar and (net) longwave radiation• sensible and latent heat fluxes• ground heat flux• snowmelt timing (and energy fluxes)

Hence, it may be more useful to consider temperature sensitivity

Two approaches to estimating sensitivities:

a) From observations (with inherent record length, and perhaps stationarity complications) and

b) From models (with inherent model dependence)

ΨP over the continental U.S. (from Sankarasubramanian and Vogel, WRR, 2001)

Precipitation elasticity ΨP as a function of Budyko humidity index over the continental U.S.

•Upper plot: Hydrologic regions 1, 3, 12 (New England, SE, Texas)

•Lower plot: Hydrologic regions 10 and 17 (Missouri and Pacific NW)

Source: Sankarasubramanian and Vogel, WRR, 2001

Precipitation elasticity ΨP as a function of mean accumulated snow depth

Source: Sankarasubramanian and Vogel, WRR, 2001

observed

Bivariate Precipitation-temperature sensitivities inferred from naturalized Colorado River streamflows at Lees Ferry, and from simulated Lees Ferry flows

Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography

simulated

Observed – annual T

Bivariate Precipitation-temperature sensitivities inferred from naturalized Colorado River streamflows at Lees Ferry, annual and winter T

Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography

Observed – winter T

Visual courtesy Hugo Hidalgo, Scripps Institution of Oceanography

Bivariate Precipitation-temperature elasticities inferred from naturalized Colorado River streamflows at Lees Ferry, and from simulated Lees Ferry flows

Precipitation elasticityas a function of precipitation difference (T = 0) from Colorado River at Lees Ferry naturalized annual flows, 1905-2006. Upper plot unsmoothed, lower smoothed.

Annual basin precipitation elasticity from VIC model (20-year simulation), with +10% precipitation increase (~1.9 for basin at outlet)

Elasticity

Runoff sensitivity to 1o C increase in Tmin and Tmax (downward solar radiation constant) Runoff from cells

with negative sensitivity

Runoff from cells with negative sensitivity

Spatial distribution of runoff sensitivity to 1o C increase in Tmin and Tmax (downward solar radiation constant)

Basin aggregate: 2.2% per oC

Runoff sensitivity to 2o C increase in Tmax and no increase in Tmin (changes both vpd and downward solar radiation)

Basin aggregate: 3.3% per oC

So is there, or is there not, a dichotomy?

Very roughly, mid-century ΔP 18%, so for = 1.5-1.9, and temperature sensitivity 0.02-0.03, and ΔT 2 oC, ΔQ 35% (vs > 50% + from GCM)

More important, though, is the question: does the land surface hydrology matter, or does the land surface just passively respond to changes in the atmospheric circulation?

i.e., in the long-term mean, VIMFC P-E Q, so do we really need to know anything about the land surface to determine the runoff sensitivity (from coupled models)?

OR is the coupled system sensitive to the spatial variability in the processes that control runoff generation (and hence ET), and in turn, are there critical controls on the hydrologic sensitivities that are not (and cannot, due to resolution constraints) be represented in current coupled models?

3. What are the impacts of water management on the water cycle?

~1900

2000

Construction of dams has vastly altered the water cycle by:•Altering the seasonal cycle, and annual amount of discharge (6 major global rivers, including the Colorado, no longer flow at their mouths)

•Increasing the time of travel through the channel system

•Changing the quality of rivers, and constituents and physical characteristics of continental river discharge

•Transporting water within and between rivers basins, and altering its partitioning (usually meaning increased evapotranspiration)

0

100

200

300

400

500

600

700

800

Up to1900

1901-1910

1911-1920

1921-1930

1931-1940

1941-1950

1951-1960

1961-1970

1971-1980

1981-1990

1990-1998

Nu

mb

er

of

Re

se

rvo

irs

.

Australia/New Zealand

Africa

Asia

Europe

Central and South America

North America

Reservoir construction has slowed.

All reservoirs larger than 0.1 km3

Some examples

Regulated Flow

Historic Naturalized Flow

Estimated Range of Naturalized FlowWith 2040’s Warming

Figure 1: mean seasonal hydrographs of the Columbia River prior to (blue) and after the completion of reservoirs that now have storage capacity equal to about one-third of the river’s mean annual flow (red), and the projected range of impacts on naturalized flows predicted to result from a range of global warming scenarios over the next century. Climate change scenarios IPCC Data and Distribution Center, hydrologic simulations courtesy of A. Hamlet, University of Washington.

Columbia River at the Dalles, OR

Colorado River basinIrrigation water

requirements

Evapotranspiration

increase

Changes in sensible

heat fluxes

Changes in surface

temperatures

Changes in latent heat

fluxes

0 100 200mm Percent

0 50 100 0 10 20Wm-2

-30 -20 -10 0Wm-2

-1.5 -1.0 -0.5 0 °C

• Figure: Results for three peak irrigation months (Jun, Jul, Aug), averaged over the 20-year simulation period.

• Max changes in one cell during the summer: Evapotranspiration increases from 24 to 231 mm, latent heat decreases by 63 W m-2, and daily averaged surface temperature decreases 2.1 °C

• Mean annual “natural” runoff and evapotranspiration: 42.3 and 335 mm

• Mean annual “irrigated” runoff and evapotranspiration: 26.5 and 350 mm

Colorado River basin – modelled effects of irrigation on moisture and energy fluxes

Irrigation water

requirements

Evapotranspiration

increase

Changes in sensible

heat fluxes

Changes in surface

temperatures

Changes in latent heat

fluxes

0 100 200mm Percent

0 50 100 0 10 20Wm-2

-30 -20 -10 0Wm-2

-1.5 -1.0 -0.5 0 °C

● Figure: Results for three peak irrigation months (Jun, Jul, Aug), averaged over the 20-year simulation period.

● Max changes in one cell during the summer: Evapotranspiration increases from 24 to 231 mm, latent heat decreases by 63 W m-2, and daily averaged surface temperature decreases 2.1 °C

● Mean annual “natural” runoff and evapotranspiration: 42.3 and 335 mm● Mean annual “irrigated” runoff and evapotranspiration: 26.5 and 350 mm

Water Management

Model

Hydrology Model

Atmospheric forcing (gridded observations, or downscaled from weather

or climate model)

Our typical approach to modeling water management effects within the land hydrological cycle

Some thoughts on the institutional setting

• International programs The role of WCRP (and especially GEWEX)

and the need for reinvention

• Funding agenciesThe impact of decisions by program managers,

and the need for more community involvement in the setting of priorities

“The most general problem is … the transition from a qualitative to a quantitative science ..”

(Horton, “The field, scope, and status of the science of hydrology,” Trans. AGU, 1931)

Conclusions

•We need to understand hydrologic sensitivities – to vegetation and climate change – better. There is a compelling motivation to do so both from a scientific and societal need basis.

•We need a more scientific approach to understanding the feedbacks and implications of water management and anthropogenic perturbations on the water cycle

•The time has come to rethink international programs related to land hydrology, and related U.S. funding priorities and mechanisms