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

How to Apply Process Information to Improve Prediction

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

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

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

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

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

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

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

The Response Ciaran Harman, Catchment Science Symposium, EGU 2011

The Response Ciaran Harman, Catchment Science Symposium, EGU 2011

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

Physically Lumped Properties (emergent behavior) Connectivity Thresholds Residence Time

Physically Lumped Properties Connectivity Thresholds Residence Time

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.

Physically Lumped Properties Connectivity Thresholds Residence Time

Residence Time Methods

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

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

Soil water residence time Annual Data P 2250 mm Q 1350 mm E 850 mm Average Data Slope 34o Relief 100-150m Ksat 5 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 ‰

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

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

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

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

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

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

A Tale of Two Catchments

A Natural Storage Experiment Storage Capacity

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

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

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

Distributed Soil Moisture Measurements - Aspect