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CLAPS - WATER RESOURCES ASSESSMENT IN DATA-SCARCE AREAS

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Presented at the IHE-Unesco Institute in Delft. August 2009

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CLAPS - WATER RESOURCES ASSESSMENT IN DATA-SCARCE AREAS

  1. 1. Water resources assessment in data-scarce areas Pierluigi Claps - Politecnico di Torino and HydroAid pierluigi.claps@polito.it www.idrologia.polito.it/~claps
  2. 2. PREDICTION IN UNGAUGED BASINS (PUB)
  3. 3. What does UB mean? (Really totally Ungauged?)
  4. 4. What does UB mean? (Really totally Ungauged?) NO Runoff but what about climatic data?
  5. 5. ANNUAL RUNOFF ESTIMATION IN UNGAUGED BASINS (1) Case study: Basilicata Region (10000 km2 - 22 gauged test basins)
  6. 6. Empirical Statistical Estimation of Dm (Basilicata) 1 Dm = -24,8 + 4.37 ⋅ ln Pm + 0,0028 ⋅ z 3 OK, R2=0.9552 but, how to adopt the relation outside the calibration region? Dm = average annual runoff (mm) Pm = average annual rainfall (mm) z = average basin elevation (m a.s.l.)
  7. 7. ANNUAL RUNOFF ESTIMATION IN UNGAUGED BASINS (2) Case study: Piemonte Region (25000 km2 - 47 gauged test basins) Very heterogeneous region in climate and morphology
  8. 8. Empirical Statistical estimation of Dm (Piemonte) 1 Dm = -22.7 + 4.37 ⋅ ln Pm + 0.001 ⋅ z 3 R2=0.883 (Basilicata) 1 Dm = -24,8 + 4.37 ⋅ ln Pm + 0,0028 ⋅ z 3
  9. 9. Empirical Statistical estimation of Dm (Piemonte) 1 Dm = -22.7 + 4.37 ⋅ ln Pm + 0.001 ⋅ z 3 R2=0.883 (Basilicata) 1 Dm = -24,8 + 4.37 ⋅ ln Pm + 0,0028 ⋅ z 3 Very close relations but different coefficients...
  10. 10. a simple question: 8
  11. 11. a simple question: • what to do when calibration basins are very few? 8
  12. 12. a simple question: • what to do when calibration basins are very few? 8
  13. 13. a simple question: • what to do when calibration basins are very few? • is average climate related to annual runoff? • are there other useful physical information? 8
  14. 14. IS AVERAGE CLIMATE RELATED TO ANNUAL RUNOFF ? CLIMATIC INDICES can be computed using Rainfall and ... … Temperature (Emberger, 1955): …Potential evapotranspiration (Thornthwaite, 1948) …Solar radiation (Budyko, 1956) Rn I= λ⋅P ETp = average annual potential evapotranspiration (mm) P = average annual rainfall (mm) M = mean temperature of the hottest month (K) Rn = average annual net radiation (MJ/m2) m = mean temperature of the coldest month (K) λ = latent heat of vaporization (MJ kg-1) €
  15. 15. Selection of the minimum necessary information, for use in data-scarce areas • are Climatic Indices meaningful? • are they related to runoff? • how much information is really necessary to compute them?
  16. 16. climatic variables in Basilicata (partially reconstructed) NE MEAN ANNUAL RAINFALL MEAN ANNUAL TEMPERATURE 41 2400 2200 2000 40.5 1800 Latitudine 1600 1400 40 1200 1000 800 39.5 600 14.5 15 15.5 16 16.5 17 Longitudine MEAN ANNUAL PET NET RADIATION
  17. 17. 1. Comparison of different climatic indices (Claps & Mancino, 2002) HUMID HUMID ARID ARID ARID (x-µ)/σ HUMID
  18. 18. 2. Budyko Index and Annual Runoff
  19. 19. 3. Evaluation of the minimum necessary amount of information BASIC VARIABLES CLIMATIC VARIABLES Terrain Elevation Temperature Latitude Net Radiation Average Cloudiness factor (relative eliophany) Precipitation Evapotraspiration
  20. 20. 3. Evaluation of the minimum necessary amount of information BASIC VARIABLES CLIMATIC VARIABLES Terrain Elevation Temperature Latitude Net Radiation Average Cloudiness factor (relative eliophany) Precipitation Evapotraspiration
  21. 21. 3. Evaluation of the minimum necessary amount of information BASIC VARIABLES CLIMATIC VARIABLES Terrain Elevation Temperature Latitude Net Radiation Average Cloudiness factor (relative eliophany) Precipitation Evapotraspiration
  22. 22. 3. Evaluation of the minimum necessary amount of information BASIC VARIABLES CLIMATIC VARIABLES Terrain Elevation Temperature Latitude Net Radiation Average Cloudiness factor (relative eliophany) Precipitation Evapotraspiration
  23. 23. 3. Evaluation of the minimum necessary amount of information BASIC VARIABLES CLIMATIC VARIABLES Terrain Elevation Temperature Latitude Net Radiation Average Cloudiness factor (relative eliophany) Precipitation Evapotraspiration
  24. 24. Empirical T(z,Lat) estimation (Claps and Sileo, 2001) stations in Southern Italy Mean annual Temperature (°C) Mean monthly Temperature
  25. 25. Empirical T(z,Lat) estimation (Claps and Sileo, 2001) 25 stations in Southern Italy Temperatura media mensile °C 20 15 Mean annual Temperature (°C) 10 5 0 Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic Pescopagano 25 Mean monthly Temperature Temperatura media mensile °C 20 15 10 5 0 Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic Melfi 30 Temperatura media mensile °C 25 20 15 10 5 0 Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic Policoro
  26. 26. Empirical T(z,Lat) estimation (Claps and Sileo, 2001) 25 stations in Southern Italy Temperatura media mensile °C 20 15 Mean annual Temperature (°C) 10 5 0 Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic Pescopagano 25 Mean monthly Temperature Temperatura media mensile °C 20 15 10 5 0 Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic Melfi 30 Temperatura media mensile °C 25 20 15 10 5 Relations affected by the scale 0 Gen Feb Mar Apr Mag Giu Lug Ago Set Ott Nov Dic Policoro of the analysis?
  27. 27. Reconstruction of average monthly temperature (Claps et al., 2008) > 700 stations in Italy
  28. 28. Morphological Variables (1) Ds = geometric mean of the distance from the sea in the eight cardinal directions (Continentality)
  29. 29. Morphological Variables (2) As = combined measure of aspect (orientation) and sea proximity
  30. 30. Morphological Variables (3) C = concavity index, obtained by weighting the azimuthal angle in the eight directions (obstruction)
  31. 31. Monthly values: observed variability Fourier reconstruction
  32. 32. Most efficient models found for amplitude and phase of the first Fourier harmonic E = Elevation L = Latitude 21
  33. 33. Average Annual temperature in Italy
  34. 34. Precipitation data?
  35. 35. From Satellite images (GIMMS - http://glcf.umiacs.umd.edu) Normalized Difference Vegetation Index (NDVI) Depends on measures of reflectance in the Visible (RVIS) and in the Near-Infrared (RNIR) : Claps and Laguardia, 2004
  36. 36. MEAN NDVI AND CLIMATIC DESCRIPTORS AT BASIN SCALE
  37. 37. MEAN NDVI AND CLIMATIC DESCRIPTORS AT BASIN SCALE
  38. 38. MEAN NDVI AND CLIMATIC DESCRIPTORS AT BASIN SCALE saturation
  39. 39. NDVI and MEAN ANNUAL RUNOFF
  40. 40. NDVI and VARIANCE OF THE ANNUAL RUNOFF
  41. 41. larger scale, many different conditions
  42. 42. The CUBIST Project (Min. of Education, Italy) 29
  43. 43. ~ 500 basins with runoff data Maximum annual instantaneous and daily discharge; several daily runoff time series, etc. ~ 6000 rainfall stations Maximum annual daily rainfall, max annual rainfall in 1-24 hrs (40% of the stations), etc.
  44. 44. The Information System of the Italian basins - fully open source (grass-postgres-openI) - GIS raster and vector database-compliant - compatible with the CUAHSI information system
  45. 45. intersection between raster data (kriged IDF scale parameter) and basin perimeters !"#$%$$$&''()*+ ! ! 32
  46. 46. NEXT STEP ON THE LARGE SCALE: seasonality of NDVI vs seasonality of runoff Fourier analysis on the monthly values
  47. 47. Preliminary application to MOPEX basins (cooperation with Univ. of Arizona) 34
  48. 48. seasonality of NDVI vs seasonality of runoff • Average NDVI (16 days-values) for each catchment • Distance between the NDVI in 2 catchments d = mean|NDVI1,i – NDVI2,i| • Distances for each pairs of curves  distance matrix DNDVI • Analugous distance for monthly streamflow regime curve All NDVI regimes (431 MOPEX catchments) |NDVI1,i–
  49. 49. NDVI in Italy (awaiting for application) A parameters of the first harmonic F (months)
  50. 50. thanks, and come to Turin! papers on the topic available at: 37 http://www.idrologia.polito.it/risorseidriche/download.html

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