1. D.L. Farmer (1), M. Sivapalan (1), and I. Lockley (2) Assessing vegetation influence on water balance in rehabilitation landscapes using simple storage models (1) Centre for Water Research, University of Western Australia, Nedlands, WA 6907 (2) Alcoa World Alumina Australia, Applecross, WA 6153

2. * rehab. survival * maximise water ‘interception’ * low permeability base, perc = f(H) * lateral flows (landscape) * resolve management needed Why ? * shallow profile 2-4m * residue sand (~homog) * potentially small gradients * water use = f(trees) Situation...

4. Simple 1D storage based model (dynamic) : * vegetation: LAI, root depth vegetation ‘grown’ in model * stores:  root,  unsat, H wt store balances computed daily * fluxes: ET, Q perc, Q wt-root Q = func(  fc) [simple threshold approach] Q = suction flow across unsat zone * recorded data: P, Ep also Irrigation, Q out lumped water content in 3 zones - root, unsat (root-Hwt), WT Shallow WT homogenous, permeable sand no lateral flows ET = ?

5. Why not Richards Equation ? (at least initially) Problem with more complex formulations: * the computational overhead (to a lesser extent) * the need to resolve within the discretised domain exactly which layer and how much water is extracted by trees. * substantial increase in parameters to cover soil diffusive effects, root distribution, root growth, water use with extraction depth (e.g. changing head effects). Such information is extremely difficult to obtain for landscapes. * increase model complexity…..not necessarily more correct results Ultimate goal is to compute indicative estimates over a landscape, potentially for coupling to a groundwater array……. SIMPLE Need facility to experiment and analyse water balance, less process dependence makes it easier to generate understanding.

6. Literature values………vegetation water use….. Source: Raper, 1998 Conclusion : max 2mm/day 5mm/day 2.5mm/day Eagleson WEC-C / Alcoa [mine rehab]

7. ** details in paper Water balance model ET ‘model’ idea: to experiment with various options * realistically ET = func [biomass (LAI), root depth,  root, ‘stresses’] * ability to set maximum threshold, to explore ‘acceptable’ ET ranges * assumption that trees would grow !! [set by defined t, LAI ] *

8. 30m x 30m cells (3m, 2m, 2m, 2m bare) multiple piezometers (weekly), monthly soil moisture, Q out, 6 monthly veg. measurement [plus Alcoa refinery met data]

9. Results in 3 phases 1. Long term behaviour 2. Recharge 3. Water use

10. Long term predictions * supported by E.German lysimeter, C. Hinz * based on residue sand data, and rehab LAI values

11. Recharge simulations

12. Water use simulation and episodic recharge….. M=unlimited evap, red=fc

13. Long term excess water prediction…. (provided expected values attained)

14. Conclusions: * would appear that the simple model is capable of capturing the essence of the water table behaviour, particularly in discriminating recharge rainfall events (though no long term continuous data as yet). * evapotranspiration remains an issue, hopefully cells and ongoing work will begin to answer some of these questions. (but expect that >2.5mm based on current data) * in many situations the water balance of the root zone is more critical than total storage within the system. Simple approach offers means to meaningfully assess potential recharge variability. Further application: * representation can be even simpler, particularly in ‘mature’ and forested landscapes. (though extraction ratios between unsat/WT an issue) * capacity to use in catchment water balance models to simply simulate unsaturated water balance (failing of many hydrology models in SW regions). * sufficiently simple for coupling to shallow aquifer GW models to overcome ‘constant recharge’ and ‘episodic recharge’ issues. * potential to extend idea to ‘hillslope’ models looking at integrated SWM for indicative analysis (low relief landscapes).