UNESCO-HELP BASIN         The Alento River Basin  Presentation of study areas and results              N. Romano and G.B. ...
RationaleMajor limitations on current studies of modeling hydrologicprocesses and assessing the impacts of landuse and cli...
The SPERAS project  [from the Latin-root verb: speras  you expect (something of good)] S oil P rocesses and E co-hydrolog...
Who is involved?
The Alento River BasinCampania RegionSalerno ProvinceCilento area
Alento River at “Piano della Rocca” damElevation                    96 m a.s.l.Water surface area     ha    max 200 – min ...
Study area: Upper Alento River basin
Upper Alentohydrographic  network
Landuse in 1955
Landuse in 1998
field campaigns to set-up a   soil – landscape map
Soil-landscape mapsampling soils alonghillslope transects
Experimental site                               Alento River basin            Subhumid climate            Annual rainfall ...
Field hydrological monitoring   EGU 2010, Vienna
Field hydrological monitoring   EGU 2010, Vienna                                   Weather                                ...
Field hydrological monitoring   EGU 2010, Vienna             V-notch weir
Field hydrological monitoring       EGU 2010, Vienna                    TDR grid sampling
Field hydrological monitoring       EGU 2010, Vienna  Local soil water content and  soil water potential monitoring
Field hydrological monitoring   EGU 2010, Vienna  Stone-cased well
monitoring soil water contents        with TDR100
soil properties: field and lab investigationsClay soil, with vertic features (vertisols)Large and deep cracks within soil ...
Simultaneousdetermination of soilhydraulic properties usingthe evaporation method.(Romano and Santini, WRR, 1999)
soil properties: field & lab investigation  Low saturated hydraulic conductivity of the soil matrix (<0.8 mm/h)  High perm...
identifying dominant hydrologic states                                     EGU 2010, ViennaRAIN                           ...
surficial soil moisture variabilitySurface soil moisture have been measured according to a25m sample grid in 12 field camp...
surficial soil moisture variability Data      N                  CV              KS01/09/06   56   0.257   0.074   0.28...
surficial soil moisture variability Data       N                  CV              L-Ntest   Lilliefors test for        ...
surficial soil moisture variabilityDuring transition periods, surface soil moisture assumes abimodal distribution as a res...
surficial soil moisture variability  During transition periods, surface soil moisture assumes a  bimodal distribution as a...
surficial soil moisture variabilityDuring transition periods, surface soil moisture assumes abimodal distribution as a res...
what we have learned (up to now) …• We have identified 4 different periods that  characterize the hydrologic response of t...
Space-based earth observation and in-depthanalyses of natural phenomena characterizingenvironmental evolution offer new pe...
20 July 2004                                       24 Oct. 2004 soil, vegetation, and landscape characterization through s...
LAI    ETp                     (mm/d)Image on18 June 2004
ETp               LAI   (mm/d)image on20 July 2004
KEY TO PROGRESSAbout the data … : improving our monitoring techniques over a broad range of scales (to measure/infer soil...
Alento riverarea presentation
Alento riverarea presentation
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Alento riverarea presentation

  1. 1. UNESCO-HELP BASIN The Alento River Basin Presentation of study areas and results N. Romano and G.B. ChiricoDepartment of Agricultural Engineering - University of Napoli Federico II
  2. 2. RationaleMajor limitations on current studies of modeling hydrologicprocesses and assessing the impacts of landuse and climatechanges are lack of:• good quality observational data and model parameters, especially the soil hydraulic characteristics, to provide a basis for evaluation of hydrologic model performance and reliable scenario construction;• information on how the nature of spatial variability of soils (parameters) and boundary conditions (data) affects hydrologic response over a range of scales;• in-depth understanding of effectiveness of using different modeling tools for soil moisture dynamics (for example, the bucket model vs. the Richards equation); and,• clear identification of the catchment landscape units controlling storm runoff generation, its timing, and mixing dynamics.
  3. 3. The SPERAS project [from the Latin-root verb: speras  you expect (something of good)] S oil P rocesses and E co-hydrological R esponse in the A lento river S ystemThe SPERAS Project is viewed as a box, whose contents are contributions from different ongoing projects and various other activities.
  4. 4. Who is involved?
  5. 5. The Alento River BasinCampania RegionSalerno ProvinceCilento area
  6. 6. Alento River at “Piano della Rocca” damElevation 96 m a.s.l.Water surface area ha max 200 – min 100Length km max 3.9 – min 1.0Depth max 34 mPerimeter km 9.3Wood protection belt ha 154
  7. 7. Study area: Upper Alento River basin
  8. 8. Upper Alentohydrographic network
  9. 9. Landuse in 1955
  10. 10. Landuse in 1998
  11. 11. field campaigns to set-up a soil – landscape map
  12. 12. Soil-landscape mapsampling soils alonghillslope transects
  13. 13. Experimental site Alento River basin Subhumid climate Annual rainfall 1200 mm Average air temperature 15°C Area Elevation Slope Aspect ha m a.s.l. % 5.1 401 7 West
  14. 14. Field hydrological monitoring EGU 2010, Vienna
  15. 15. Field hydrological monitoring EGU 2010, Vienna Weather Station
  16. 16. Field hydrological monitoring EGU 2010, Vienna V-notch weir
  17. 17. Field hydrological monitoring EGU 2010, Vienna TDR grid sampling
  18. 18. Field hydrological monitoring EGU 2010, Vienna Local soil water content and soil water potential monitoring
  19. 19. Field hydrological monitoring EGU 2010, Vienna Stone-cased well
  20. 20. monitoring soil water contents with TDR100
  21. 21. soil properties: field and lab investigationsClay soil, with vertic features (vertisols)Large and deep cracks within soil surface duringdry periodsMacropores and roots in the top 40 cm (A-horizon)Almost permanently saturated below 150 cmDeep clay C-horizon 0% 20% 40% 60% 80% 100% Soil layers 0 A (clay) 19 31 51 40 B (clay) Depht(cm) 14 29 57 60 18 25 57 BC (clay) 100 11 29 61 C (clay) Sand Silt Clay
  22. 22. Simultaneousdetermination of soilhydraulic properties usingthe evaporation method.(Romano and Santini, WRR, 1999)
  23. 23. soil properties: field & lab investigation Low saturated hydraulic conductivity of the soil matrix (<0.8 mm/h) High permeability of the A-horizon, through preferential flow-pathsA-horizon Ks>10 mm/hB-horizon Ks<0.8mm/hC-horizon Ks<0.2mm/h Stone-cased well
  24. 24. identifying dominant hydrologic states EGU 2010, ViennaRAIN ETodry period dry wet wet to to period dry wetFlow Wells
  25. 25. surficial soil moisture variabilitySurface soil moisture have been measured according to a25m sample grid in 12 field campaigns. Soil water content map 22/09/06 Soil water content map 29/09/06 Soil water content map 03/11/06Soil water content map 2/03/07 Soil water content map 22/01/07 Soil water content map 08/12/07
  26. 26. surficial soil moisture variability Data N   CV  KS01/09/06 56 0.257 0.074 0.289 0.148 N positive skewness22/09/06 63 0.342 0.071 0.208 -0.126 N in dry state29/09/06 91 0.359 0.080 0.224 -0.255 NN03/11/06 92 0.334 0.064 0.193 -0.559 N08/12/06 92 0.405 0.066 0.163 -0.572 N22/01/07 91 0.410 0.073 0.177 -0.896 N02/03/07 92 0.408 0.076 0.187 -0.452 N As soil water content is a bounded variable, its16/03/07 91 0.347 0.091 0.261 -0.051 NN skewness decreases10/04/07 78 0.405 0.079 0.196 -0.506 N from positive to negative11/05/07 26 0.379 0.110 0.290 -0.964 N values from dry to wet9/07/07 18 0.207 0.088 0.424 0.508 N periods.12/11/07 92 0.383 0.073 0.191 -0.748 N
  27. 27. surficial soil moisture variability Data N   CV  L-Ntest Lilliefors test for goodness of fit to a01/09/06 56 0.257 0.074 0.289 0.148 N normal distribution22/09/06 63 0.342 0.071 0.208 -0.126 N at 5% significance level29/09/06 91 0.359 0.080 0.224 -0.255 NN03/11/06 92 0.334 0.064 0.193 -0.559 N non-normal08/12/06 92 0.405 0.066 0.163 -0.572 N distribution in22/01/07 91 0.410 0.073 0.177 -0.896 N transition periods02/03/07 92 0.408 0.076 0.187 -0.452 N16/03/07 91 0.347 0.091 0.261 -0.051 NN10/04/07 78 0.405 0.079 0.196 -0.506 N11/05/07 26 0.379 0.110 0.290 -0.964 N9/07/07 18 0.207 0.088 0.424 0.508 N12/11/07 92 0.383 0.073 0.191 -0.748 N
  28. 28. surficial soil moisture variabilityDuring transition periods, surface soil moisture assumes abimodal distribution as a result of the combination of verticalfluxes and lateral fluxes through preferential flow-paths.
  29. 29. surficial soil moisture variability During transition periods, surface soil moisture assumes a bimodal distribution as a result of the combination of vertical fluxes and lateral fluxes through preferential flow-paths. Soil water content map 29/09/06dry-to-wet
  30. 30. surficial soil moisture variabilityDuring transition periods, surface soil moisture assumes abimodal distribution as a result of the combination of verticalfluxes and lateral fluxes through preferential flow-paths. wet-to-dry
  31. 31. what we have learned (up to now) …• We have identified 4 different periods that characterize the hydrologic response of the hillslope; in each of which there occur different dominant hydrologic processes.• Spatial variability of surficial soil water content shows slightly different statistical features in each of these periods.• This type of investigation can give useful directions when one should build hydrologic models as related to specific objectives of modeling
  32. 32. Space-based earth observation and in-depthanalyses of natural phenomena characterizingenvironmental evolution offer new perspectiveson management of land and water resources. GIS + Earth + Model Observation u  z  u*  z  ln   k  z0m  R A R T0 X T m RS C T S (z,t) v(x,y,t)
  33. 33. 20 July 2004 24 Oct. 2004 soil, vegetation, and landscape characterization through satellite images
  34. 34. LAI ETp (mm/d)Image on18 June 2004
  35. 35. ETp LAI (mm/d)image on20 July 2004
  36. 36. KEY TO PROGRESSAbout the data … : improving our monitoring techniques over a broad range of scales (to measure/infer soil hydraulic properties & fluxes at scales of interest for environmental planning).About the models … : identifying dominant vegetation, soil and topography controls on ecosystem dynamics.Defining new criteria for moving across scales

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