![Details about soil variability (~Fiori and Russo, 2007)
19
Homogeneous
s2 = 0.3 s2 = 1.0
s2 CV s2 CV
Ksat [m/h] 0.02 (geom.) 0.3 0.6 1.0 1.3
a [m-1] 3.5 (geom.) 0.2 0.4 0.5 0.8
n [-] 2.0 (arithmetic) 0.02 0.05 0.05 0.1
qs [-] 0.42 (arithmetic) 0.001 0.05 0.002 0.1
Ksat [m/h] a [m-1] n [-] qs [-]
Ksat [m/h] 1
a [m-1] 0.8 1
n [-] 0.4 0.5 1
qs [-] -0.4 -0.2 -0.6 1
Correlation matrix](https://image.slidesharecdn.com/baronipadova-151009092420-lva1-app6891/85/Gabriele-Baroni-19-320.jpg)
Gabriele Baroni
- 1. 24/09/2015 Padova (IT) On the role of subsurface heterogeneity at hillslope scale with Parflow Gabriele Baroni Sabine Attinger
- 2. Outline Quick overview of the project Motivations in our research unit The effect of soil heterogeneity at field/hillslope Outlook 2
- 3. Overview project German funded project (DFG) "Data Assimilation for Improved Characterization of Fluxes across Compartmental Interfaces“ Seven research units working on different compartments of the terrestrial system 3 http://www.for2131.de/home-en
- 4. Overview project and our unit 1. To create a virtual reality at catchment scale with an integrated model (TerrSysMP) 2. Coarsening the model with different upscaling rules and see the effects 3. To develop a unified data assimilation framework to improve the performance of the model • which measurements to integrate? • how? 4 Test at small scales
- 5. Motivations heterogeneity and scaling effects Whenever we apply the current distributed models (e.g. Richards eq.) we assume uniform parameters within the grid Whatever is the scale of application (lab, field, catchment) we do an upscaling exercise 5
- 6. Research questions in this upscaling exercise? we have to find upscaling rules/effective parameters • different results functions of domain set-up, parameters distributions, boundary conditions etc. • Holy Grail of hydrology…worth searching for even if a general solution might ultimate prove impossible to find (Beven, 2006) 6
- 7. Hillslope Rain/runoff Flat field Infiltration/drainage Tests at field/hillslope with Parflow Variability in soil properties (~Fiori and Russo, 2007) 7 1 Homogeneous 2 s2= 0.3 ~ergodic domain = 33 * Integral scale 3 ~non-ergodic domain = 5 * Integral scale 4 s2= 1.0 ~ergodic domain = 33 * Integral scale 5 ~non-ergodic domain = 5 * Integral scale
- 8. Same mean but variability Flat field Infiltration/drainage at 10 cm depths 8 Homogeneous Mean and variance of soil moisture and pressure over the plan - 10 cm - 10 cm
- 9. Flat field: mean soil moisture and pressure 9 • Soil moisture dynamics well represented by the homogeneous counterpart • To check if differences are within statistics of the random fields (single realization vs. ensemble of 50 realizations)
- 10. Flat field: mean soil moisture and pressure 10 • Differences if s2 increases • To check effect of this dynamic non-equilibrium in longer term t0 t1 t2 t0 t1 t2
- 11. Hillslope storage-discharge 11 • Storage dynamics well represented by the homogeneous counterpart • To check if differences are within statistics of the random fields (single realization vs. ensemble of 50 realizations)
- 12. Hillslope storage-discharge 12 • Storage dynamics well represented by the homogeneous counterpart • To check if differences are within statistics of the random fields (single realization vs. ensemble of 50 realizations) • Heterogeneity = faster responses • Some differences especially if non-ergodic
- 13. To summarize so far… With an homogeneous counterpart state dynamic well represented (i.e., soil moisture or water storage) variability increases dynamic non–equilibrium but to test implications at longer term non-ergodicity - more than variability - precludes the use of general upscaling rules 13
- 14. To come… To generalize the tests at field/hillslope scale to better understand the role of soil heterogeneity on the hydrological responses To finalize the virtual reality and to analyse the effect of coarsening with different upscaling rules at catchment scale 14
- 15. …but a working hypothesis 15 Model results GOOD BAD Fine grid Coarse grid e.g., topographysoil Ergodic D>>I grid resolution From searching for effective parameters to search for best resolution? grid resolution GOOD BAD Fine grid Coarse grid Model results ? Best resolution? Non-ergodic D ~ I at this scale we might still have uncertainty in state and discharge (fluxes): DA framework to integrate both measurements and to compensate the model structure uncertainty
- 16. Thank you for the attention 16
- 17. Virtual reality 17 Neckar Catchment: Location (Baden-Württemberg, Germany) Area 14,000 km2 Temperate-Humid climate Average annual precipitation 950mm Medium groundwater depth (1-2m) Model set-up Different virtual realities ~ resolution 50 – 800 m ~ 30 million nodes ~ 5 -12 years of simulation runs We aim at a reasonable approximation Plausible check with measurements
- 18. Details of model set-up 18 1 Homogeneous 2 s2= 0.3 ~ergodic domain = 33 * Integral scale 3 ~non-ergodic domain = 5 * Integral scale 4 s2= 1.0 ~ergodic domain = 33 * Integral scale 5 ~non-ergodic domain = 5 * Integral scale Soil Domain 50 x 50 x 50 nodes 1 m resolutions xy dZ verticalbedrock 1.4 m 18.6 m 50 m 50 m
- 19. Details about soil variability (~Fiori and Russo, 2007) 19 Homogeneous s2 = 0.3 s2 = 1.0 s2 CV s2 CV Ksat [m/h] 0.02 (geom.) 0.3 0.6 1.0 1.3 a [m-1] 3.5 (geom.) 0.2 0.4 0.5 0.8 n [-] 2.0 (arithmetic) 0.02 0.05 0.05 0.1 qs [-] 0.42 (arithmetic) 0.001 0.05 0.002 0.1 Ksat [m/h] a [m-1] n [-] qs [-] Ksat [m/h] 1 a [m-1] 0.8 1 n [-] 0.4 0.5 1 qs [-] -0.4 -0.2 -0.6 1 Correlation matrix
- 20. Energy and mass fluxes in TerrSysMP 20 Gasper al, 2015