1. 11/1/2011 Summary statement on runoff generation
You summarized the classic “named” mechanisms
My view – 2 requisite conditions 1 2
Key task: incorporate process knowledge into predictive models
Next Project

2. How to Apply Process Information to Improve Prediction

3. Improved prediction and improved process understanding are mutually reliant
time
Precipitation
time
flow

4. Perceived Intellectual Value:
Modified from Mukesh Kumar
Lumped Model
Semi-Distributed Model, Conceptual
Distributed Model,
Physics based
REW 1
REW 2
REW 3
REW 4
REW 5
REW 6
REW 7 p q q q
Parametric
Physics-Based
Process Representation:
Predicted States Resolution:
Coarser
Fine
Data Requirement:
Small
Large
Computational Requirement:
Small
Large
Perceived Intellectual Value: 4

5. Modified from Mukesh Kumar
Lumped Model
Semi-Distributed Model, Conceptual
Distributed Model,
Physics based
REW 1
REW 2
REW 3
REW 4
REW 5
REW 6
REW 7 p q q q
Right for Wrong Reasons
Wrong for Right Reasons
Outcome:
Mathematical Lumping
Process Understanding
History: ?
Process Understanding
Future: 5

6. How do we use Process Knowledge or data in this scene?
time
Precipitation
flow

7. How do we use Process Knowledge or data in this scene?
time
Precipitation
flow
Calibration
Assumes “model” is correct, forces parameters to give the right answer
Rewrite model to properly represent processes

8. In Defense of Hydrologic Reductionism
… an approach to understand the nature of complex things by reducing them to the interactions of their parts…
…a philosophical position that a complex system is nothing but the sum of its parts, and that an account of it can be reduced to accounts of individual constituents …
My Past
Berkely Catchment Science Symposium 2009

9. My Past: In Defense of Reductionism
Newton was right
Model failures result from poor characterization of heterogeneous landscapes leads to
No emergent properties
Our community struggles to identify grand, overarching questions because…there are no grand unknowns
Hydrology is a local science

10. The Response Ciaran Harman, Catchment Science Symposium, EGU 2011

11. The Response Ciaran Harman, Catchment Science Symposium, EGU 2011

12. Catchments Lump Processes
Emergent Behavior
Decades of case studies have documented the many ways that water moves downhill
Recent work has identified many Physically Lumped Properties that are manifestations of the system of states and fluxes
-A physical basis for lumped parameter modeling

13. Physically Lumped Properties (emergent behavior)
Connectivity
Thresholds
Residence Time

14. Physically Lumped Properties
Connectivity
Thresholds
Residence Time

15. Threshold responses 1 Runoff ratio 10 20 30 40 50 Moisture content (%)
Satellite
Tarrawarra
Runoff ratio 10 20 30 40 50
Moisture content (%)
Courtesy of Roger Grayson
Roger Grayson, pers. Com.

16. Physically Lumped Properties
Connectivity
Thresholds
Residence Time

17. Residence Time Methods

18. Figure from Jim Kirchner
This approach simplified ) ( t C in
out
Figure from Jim Kirchner

19. Model Theory: The Convolution Integral
Predicted or simulated output d18O signature
Input Function:
Derived from precipitation d18O signal
Represents d18O in water that contributes to recharge
System Response Function:
Time distribution of water flow paths

20. Soil water residence time
Annual Data
P mm
Q mm
E mm
Average Data
Slope 34o
Relief m
Ksat m/hr
Soils Data
Depth 1 m
Strong catenary sequence
Soil water
Residence
Time -4 -8
-12
-16
d18O‰
Soil Water
Precipitation
Average -9.4‰
Amplitude 0.1‰
Std Dev. 3.4 ‰
Amplitude 1.2‰
Std Dev. 0.6 ‰

21. If bedrock quite impermeable MRT and distance from the divide
Vache and McD WRR 2005

22. Process Understanding Process Understanding
Modified from Mukesh Kumar
Lumped Model
Semi-Distributed Model, Conceptual
Distributed Model,
Physics based
REW 1
REW 2
REW 3
REW 4
REW 5
REW 6
REW 7 p q q q
History:
Mathematical Lumping
Process Understanding ?
Process Understanding
Future: 22

23. Physically lumped properties
Modified from Mukesh Kumar
Distributed Model,
Physics based
Physically Lumped Model
Physically lumped properties q
History:
Mathematical Lumping
Process Understanding
Process Understanding
Physically lumped properties
Future: 23

24. How to Apply Process Information to Improve Prediction
Retain the computationally efficiency and lumped philosophy of systems models
Observe how catchments create physically lumped properties
Replace mathematical lumping approaches with physically lumped properties
Use as “processes” , not data as validation targets
Build “processes” into new model structures

25. What do we do with this awareness?
Connectivity
Thresholds
Residence Time

26. Lump the lumps It’s about Storage
P-ET-Q =dS/dt
Storage
Connectivity
Thresholds
Residence Time

27. A Tale of Two Catchments

28. A Natural Storage Experiment
Storage Capacity

29. A Natural Storage Experiment
P-ET-Q =dS/dt
Storage Capacity
We should focus on Runoff Prevention mechanisms in addition to runoff generation mechanisms
We should concern ourselves with how catchments Retain Water in addition to how they release water

30. The Storage Problem Storage is not commonly measured
Storage is often estimated as the residual of a water balance
Storage is treated as a secondary model calibration target

31. Improved storage characterization will lead to improved prediction
Reynolds Creek
Dry Creek

32. Distributed Soil Moisture Measurements - Aspect