1. 125 Years of Hydrologic Change in the Puget Sound Basin: The Relative Signatures of Climate and Land Cover
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Purdue Climate Change Research Center
October 20, 2008

2. Outline of this talk The role of hydrology in Earth system science
What are the grand challenges in hydrology?
Understanding hydrologic change: The Puget Sound basin as a case study
Modeling background
The physical setting
Analysis and prediction
Weak links and some thoughts on the path forward

3. 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?”

4. The land hydrologic cycle in a modeling context

5. A classical hydrological problem: Predicting runoff and streamflow given precipitation

6. Runoff generation mechanisms
1) Infiltration excess – precipitation rate exceeds local (vertical) hydraulic conductivity -- typically occurs over low permeability surfaces, e.g., arid areas with soil crusting, frozen soils
2) Saturation excess – “fast” runoff response over saturated areas, which are dynamic during storms and seasonally (defined by interception of the water table with the surface)

8. Infiltration excess flow (source: Dunne and Leopold)

9. Runoff generation mechanisms on a hillslope (source: Dunne and Leopold)

11. Saturated area (source: Dunne and Leopold)

12. Seasonal contraction of saturated area at Sleepers River, VT following snowmelt (source: Dunne and Leopold)

13. Expansion of saturated area during a storm (source: Dunne and Leopold)

14. 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?

15. 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.

16. 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?

17. Understanding hydrologic change: The Puget Sound basin as a case study

18. Topography of the Puget Sound basin

19. The role of changing land cover – 1880 v. 2002

20. The role of changing climate, 1950-2000
source: Mote et al (2005)

21. Insert lan’s temperature plots here

22. Understanding hydrologic change: The modeling context
Fundamental premises
Simulation modeling must play a central role, because we rarely have enough observations to diagnose change on the basis of observations alone (and in the future, the “experiment” hasn’t yet been performed)
If the hydrological processes are changing, we need to represent those processes
Hence, prediction approaches that are “trained” to observations won’t work well

23. Distributed vs Spatially Lumped Hydrologic Models
Smaller Sub-watersheds
More realistic Processes
Lumped
Conceptual
Fully Distributed
Physically-based
Streamflow (at predetermined points)
Predictive skill limited to calibration conditions
Streamflow Snow Runoff Soil Moisture, etc at all points and areas in the basin
Predictive Skill Outside Calibration Conditions.
Visual courtesy Andrew Wood

24. The Distributed Hydrology-Soil-Vegetation Model (DHSVM)

26. Lumped Conceptual (Processes parameterized)
Explicit Representation of Downslope Moisture Redistribution
Lumped Conceptual (Processes parameterized)

27. DHSVM Snow Accumulation and Melt Model

28. Representing urbanization effects in DHSVM
Hydrologically relevant features of urbanization not found in “natural” watersheds:
surface components such as streets, rooftops, ditches
subsurface components such as pipes and other manmade stormwater drainage conduits
In fully urbanized catchments, these elements are linked through street curb inlets and manholes
In partially urbanized catchments, these urban drainage elements are often mixed with the natural channel drainage system

29. Modifications of DHSVM for urban areas
For pixels with land cover category “urban”, a fraction of impervious surface area is specified.
For the fraction that is not impervious, DHSVM handles infiltration using the same parameterizations as for non-urban pixels.
A second parameter, the fraction of water stored in flood detention, was also added. Runoff generated from impervious surfaces is assumed to be diverted to detention storage.
The runoff diverted to detention storage is allowed to drain as a linear reservoir, and re-enters the channel system in the pixel from which it is diverted.
Surface runoff that is not diverted is assumed to enter the channel system directly, i.e., all urban channels are connected directly to the channel system
We assume that the natural channel system remains intact, and we retain the support area concept that defines the connectivity of pixels to first order channels. However, impervious surface runoff (and drainage from detention reservoirs) is assumed to be connected to the nearest stream channel directly
Once impervious surface runoff has entered a stream channel, it follows the “standard” DHSVM channel flow routing processes.

30. Springbrook Creek catchment

31. Springbrook Creek simulations (left) and errors (right) with and without urban module
No impervious or detention
Impervious, no detention
impervious and detention

32. Springbrook Creek mean seasonal cycle simulated current land cover and all mature forest

33. Forest cover change effects
Measurement of Canopy Processes via two 25 m2 weighing lysimeters (shown here) and additional lysimeters in an adjacent clear-cut.
Direct measurement of snow interception

34. Calibration of an energy balance model of canopy effects on snow accumulation and melt to the weighing lysimeter data. (Model was tested against two additional years of data)

35. Understanding the effects of historical land cover and climate change on the Puget Sound basin – modeling and analysis

36. Study Areas Puget Sound basin, Washington State, USA
Temperate marine climate,
Precipitation falls in October – March
Snow in the highland,
rare snow in the lowland
Targeted sub-basins

37. Tmin at selected Puget Sound basin stations, 1916-2003

38. Tmin at selected Puget Sound basin stations, 1916-2003

39. Tmin at selected Puget Sound basin stations, 1916-2003

40. Model Calibration

41. Land cover change effects on seasonal streamflow for eastern (Cascade) upland gages

42. Land cover change effects on seasonal streamflow for western (Olympic) upland gages

43. Land cover change effects on seasonal streamflow at selected eastern lowland (Greater Seattle area) gages

44. Land cover change effects by region

45. Land cover change effects on annual maximum flow at eastern (Cascade) upland gages

46. Land cover change effects on annual maximum flow at western (Olympic) upland gages

47. Land cover change effects on annual maximum flows at selected eastern lowland gages (greater Seattle area)

48. Predicted temperature change effects on seasonal streamflow at eastern (Cascade) upland gages

49. Predicted temperature change effects on seasonal streamflow at western (Olympic) upland gages

50. Predicted temperature change effects on seasonal streamflow at selected eastern lowland gages (greater Seattle area)

51. Predicted temperature change effects on seasonal streamflow by region

52. Predicted temperature change effects on annual maximum flow at eastern (Cascade) upland gages

53. Predicted temperature change effects on annual maximum flows for upland gages in the western (Olympic) region

54. Predicted temperature change effects on annnual maximum flow for selected lowland gages in the greater Seattle area

55. Comparison of temperature change and land cover change effects on annual maximum flows, selected upland and lowland basins

56. Model/observed residuals analysis of selected basins with long gauge records, annual maximum and annual mean flows

57. Model/observed residuals analysis of selected basins with long gauge records, annual maximum and annual mean flows

58. Projected future climate change: GCMs
Models
Institutions
BCCR
Univ. of Bergen, Norway
CCSM3
NCAR, USA
CGCM 3.1_t47
CCCma, Canada
CGCM3.1_t63
CNRM_CM3
CNRM, France
CSIRO_MK3
CSIRO, Australia
ECHAM5
MPI, Germany
ECHO_G
Max Plank Institute for Mathematics, Germany
GFDL_CM2_1
Geophysical Fluid Dynamic Laboratory, USA
GISS_AOM
NASA/GISS, (Goddard Institute for Space Studies) USA
HADCM
Met Office, UK
HADGEM1
Hadley Center Global Environment Model, v 1., UK
INMCM3_0
Institute Numerical Mathematics, Russia
IPSL_CM4
IPSL (Institute Pierre Simon Laplace, Paris, France
MIROC_3.2
CCSR/NIES/FRCGC, Japan
PCM1

59. Projected Future Climate Conditions A1B Scenario
Basins
Tmin/Year (˚ C)
Tmax/Year
(˚ C)
Prcp/Year (%)
Tmin hist vs. future
Tmax hist vs. future
(˚C)
Prcp hist vs. future (%)
Deschutes
0.03
2.02
2.12
2.13
4.53
Cedar
0.04
1.90
1.88
1.91
3.24
Skokomish
2.16
2.04
2.05
6.59
Dosewallips
2.00
2.09
2.10
6.63
Lowland-west
2.03
2.11
6.45
Annual change rate in 2000 – 2099;
Historical vs. future change: 2000 – 2099 vs – 1999.
Average of Models: Hadgem1, Echam5, Cnrm_cm3, Hadcm, Cgcm3.1_t47, Ipsl_cm4

60. Future climate change: multi-model ensemble simulation: A1B scenario

61. Summary
Upland basin mean flow sensitivity to land cover change is mostly as a result of changes in snow accumulation and ablation, and lower ET associated with reduced vegetation.
However, overall upland basin seasonal flows distribution, especially in the transient snow zone, are much more sensitive to temperature change effects – both to mean and peak flows – than to land cover change
Lowland basin mean flows are much more sensitive to land cover change than are upland basins, especially in the most urbanized basins.
Temperature change effects on peak flows in upland basins tend to be more modest than are changes in seasonal flows (suggests rain on snow effect on peak flows may be modest).

62. Weak links and some thoughts on the path forward
(Over?) Reliance on models – the ideal design for land cover change studies is paired catchments, but …
Are the data up to the challenge?
Need to understand whether the model sensitivities are correct (not necessarily evidenced by calibration/verification errors)
How do we use evolving (e.g. remote sensing) data sources in methods that are highly dependent on long record lengths?