1. September 9, 2015 1 Today’s topics Distributed modelling 08:45 – 09:30 Distributed catchment modelling 09:45 – 10:30 Choices in degree of distribution and data Hope to give some relevant examples

2. September 9, 20152 Choice of degree of distribution How to choose the spatial representation of your model? Data availability (spatial distributed data?) Which processes are you interested in? Computational time Choose between lumped parameters or distributed parameters (equifinality)

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5. 5 Equifinality How can you justify a lot more parameters??

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7. 7 Other data sources E obs C R (FC) P n E sim Constraining on evaporation I P T (FC,L) PnPn FC R

8. September 9, 20158 Methodology Highlands Forested Dambos (wetlands) Riverine

9. September 9, 20159 Results

10. September 9, 201510 Application e.g. flood forecasting

11. September 9, 201511 Example of distributed responses

12. September 9, 201512 Application e.g. flood forecasting

13. September 9, 201513 Data sources

14. September 9, 201514 Introduction History: from point to grid  geo-statistical interpolation, e.g. Thiessen polygons (nearest neighbour) Kriging (co-variance matrix approach) Inverse distance weighted See also: lecture notes hydrological measurements General problem: by interpolating, you loose (local) extremes

15. September 9, 201515 Introduction Now: Remote sensors on satellites provide new data: …to help estimating parameters e.g…

16. September 9, 201516 Elevation Slopes Drain direction Catchment delineation Wetland and lake identification

17. September 9, 201517 Land cover Root zone depth Hydrotope delineation Estimate of interception capacity Often, links are made with extensive lookup tables (e.g. SWAT, SOBEK RR)

18. Distributed model ‘wflow’ Uses terrain analysis (derivation of flow direction, slopes, streams) Uses lookup tables to link model parameters with soil types, land cover classes

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20. September 9, 201520 Remote sensors on satellites provide new data: …to help estimating parameters e.g… …to help estimating temporally and spatially distributed data e.g…

21. September 9, 201521 Rainfall Generally based on a combination of information from different sensors Many rainfall products available Tropical Rainfall Measuring Mission (TRMM, ~25x25 km, 3-hourly) GSMaP (~10x10 km, 1-hourly) FEWS RFE 2.0 (10x10 km, daily) PERSIANN CCS (4x4 km, 30-min!!)

24. Validation and bias-correction is often required!!!

25. September 9, 201525 Energy budgets E.g. incoming solar radiation at the land surface Provides a strong indicator for the evaporative potential For Europe and Africa, LSA SAF products (see http://landsaf.meteo.pt) http://landsaf.meteo.pt

26. September 9, 201526 Energy budgets

27. September 9, 201527 Summarizing: application of remote sensing Provide input (e.g. rainfall, (potential) evaporation) May be used to constrain model structures and parameters Mitigating the ‘equifinality problem’ by incorporating the spatially distributed data in a performance criterium

28. Data structures Plain ascii (no metadata, no subsetting, sometimes complicated parser required) Plain binary floats (no metadata, no subsetting, simple parser required) Structured formats, in particular NetCDF (metadata available, subsetting very good, remote subsetting possible, no parser required)