1. UNESCO-HELP BASIN
The Alento River Basin
Presentation of study areas and results
N. Romano and G.B. Chirico
Department of Agricultural Engineering - University of Napoli Federico II

2. Rationale
Major limitations on current studies of modeling hydrologic
processes and assessing the impacts of landuse and climate
changes 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. 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 ystem
The SPERAS Project is viewed as a box, whose contents are contributions
from different ongoing projects and various other activities.

4. Who is involved?

5. The Alento River Basin
Campania Region
Salerno Province
Cilento area

6. Alento River at “Piano della Rocca” dam
Elevation 96 m a.s.l.
Water surface area ha max 200 – min 100
Length km max 3.9 – min 1.0
Depth max 34 m
Perimeter km 9.3
Wood protection belt ha 154

9. Study area: Upper Alento River basin

10. Upper Alento
hydrographic
network

11. Landuse in 1955

12. Landuse in 1998

13. field campaigns to set-up a
soil – landscape map

14. Soil-landscape map
sampling soils along
hillslope transects

15. 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

16. Field hydrological monitoring EGU 2010, Vienna

17. Field hydrological monitoring EGU 2010, Vienna
Weather
Station

18. Field hydrological monitoring EGU 2010, Vienna
V-notch weir

19. Field hydrological monitoring EGU 2010, Vienna
TDR grid sampling

20. Field hydrological monitoring EGU 2010, Vienna
Local soil water content and
soil water potential monitoring

21. Field hydrological monitoring EGU 2010, Vienna
Stone-cased well

22. monitoring soil water contents
with TDR100

23. soil properties: field and lab investigations
Clay soil, with vertic features (vertisols)
Large and deep cracks within soil surface during
dry periods
Macropores and roots in the top 40 cm (A-horizon)
Almost permanently saturated below 150 cm
Deep 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

24. Simultaneous
determination of soil
hydraulic properties using
the evaporation method.
(Romano and Santini, WRR, 1999)

25. 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-paths
A-horizon Ks>10 mm/h
B-horizon Ks<0.8mm/h
C-horizon Ks<0.2mm/h
Stone-cased well

26. identifying dominant hydrologic states
EGU 2010, Vienna
RAIN ETo
dry period dry wet wet to
to period dry
wet
Flow
Wells

27. surficial soil moisture variability
Surface soil moisture have been measured according to a
25m sample grid in 12 field campaigns.
Soil water content map 22/09/06 Soil water content map 29/09/06 Soil water content map 03/11/06
Soil water content map 2/03/07 Soil water content map 22/01/07 Soil water content map 08/12/07

28. surficial soil moisture variability
Data N   CV  KS
01/09/06 56 0.257 0.074 0.289 0.148 N
positive
skewness
22/09/06 63 0.342 0.071 0.208 -0.126 N
in dry state
29/09/06 91 0.359 0.080 0.224 -0.255 NN
03/11/06 92 0.334 0.064 0.193 -0.559 N
08/12/06 92 0.405 0.066 0.163 -0.572 N
22/01/07 91 0.410 0.073 0.177 -0.896 N
02/03/07 92 0.408 0.076 0.187 -0.452 N As soil water content is a
bounded variable, its
16/03/07 91 0.347 0.091 0.261 -0.051 NN
skewness decreases
10/04/07 78 0.405 0.079 0.196 -0.506 N
from positive to negative
11/05/07 26 0.379 0.110 0.290 -0.964 N
values from dry to wet
9/07/07 18 0.207 0.088 0.424 0.508 N
periods.
12/11/07 92 0.383 0.073 0.191 -0.748 N

29. surficial soil moisture variability
Data N   CV  L-Ntest Lilliefors test for
goodness of fit to a
01/09/06 56 0.257 0.074 0.289 0.148 N normal distribution
22/09/06 63 0.342 0.071 0.208 -0.126 N
at 5% significance
level
29/09/06 91 0.359 0.080 0.224 -0.255 NN
03/11/06 92 0.334 0.064 0.193 -0.559 N
non-normal
08/12/06 92 0.405 0.066 0.163 -0.572 N distribution in
22/01/07 91 0.410 0.073 0.177 -0.896 N transition periods
02/03/07 92 0.408 0.076 0.187 -0.452 N
16/03/07 91 0.347 0.091 0.261 -0.051 NN
10/04/07 78 0.405 0.079 0.196 -0.506 N
11/05/07 26 0.379 0.110 0.290 -0.964 N
9/07/07 18 0.207 0.088 0.424 0.508 N
12/11/07 92 0.383 0.073 0.191 -0.748 N

30. 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.

31. 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 29/09/06
dry-to-wet

32. 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.
wet-to-dry

33. 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

34. Space-based earth observation and in-depth
analyses of natural phenomena characterizing
environmental evolution offer new perspectives
on 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)

35. 20 July 2004
24 Oct. 2004
soil, vegetation, and landscape characterization through satellite images

36. LAI ETp
(mm/d)
Image on
18 June 2004

37. ETp
LAI (mm/d)
image on
20 July 2004

38. 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