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The Alento River BasinPresentation of study areas and results
Department of Agricultural Engineering - University of Napoli Federico II
N. Romano and G.B. Chirico
UNESCO-HELP BASIN
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.
The The SPERASSPERAS projectproject[from the Latin-root verb: speras you expect (something of good)]
S oilP rocesses andE co-hydrologicalR esponse in theA lento riverS ystem
The SPERAS Project
is viewed as a box, whose contents are contributions from different ongoing projects and various other activities.
Wh
o i
s in
volv
ed?
Campania RegionSalerno ProvinceCilento area
The Alento River Basin
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 1.0Depth max 34 mPerimeter km 9.3Wood protection belt ha 154
Study area: Upper Alento River basin
Upper Alento
hydrographic network
Landuse in 1955
Landuse in 1998
field campaigns to set-up a
soil – landscape map
Soil-landscape mapsampling soils along hillslope transects
Experimental site
Alento River basin
Areaha
Elevationm a.s.l.
Slope%
Aspect
5.1 401 7 West
Subhumid climateAnnual rainfall 1200 mmAverage air temperature 15°C
Field hydrological monitoring EGU 2010, Vienna
Field hydrological monitoring EGU 2010, Vienna
WeatherStation
V-notch weir
Field hydrological monitoring EGU 2010, Vienna
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 investigations
19
14
18
11
31
29
25
29
57
57
61
51
0% 20% 40% 60% 80% 100%
Dep
ht(
cm)
Sand Silt Clay
Soil layers0
40
60
100
A (clay)
B (clay)
BC (clay)
C (clay)
Clay soil, with vertic features (vertisols) Large and deep cracks within soil surface during dry periodsMacropores and roots in the top 40 cm (A-horizon)Almost permanently saturated below 150 cmDeep clay C-horizon
Simultaneous determination of soil hydraulic properties using the evaporation method.(Romano and Santini, WRR, 1999)
A-horizon Ks >10 mm/h
B-horizon Ks <0.8mm/h
soil properties: field & lab investigationLow saturated hydraulic conductivity of the soil matrix (<0.8 mm/h)High permeability of the A-horizon, through preferential flow-paths
Stone-cased well
C-horizon Ks <0.2mm/h
Wells
Flow
RAIN ETo
dry period
identifying dominant hydrologic states EGU 2010, Vienna
dry to
wet
wet period
wet to dry
surficial soil moisture variability
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
Surface soil moisture have been measured according to a 25m sample grid in 12 field campaigns.
surficial soil moisture variabilityData N CV KS
01/09/06 56 0.257 0.074 0.289 0.148 N
22/09/06 63 0.342 0.071 0.208 -0.126 N
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
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
positive skewnessin dry state
As soil water content is a bounded variable, its skewness decreases from positive to negative values from dry to wet periods.
surficial soil moisture variabilityData N CV L-Ntest
01/09/06 56 0.257 0.074 0.289 0.148 N
22/09/06 63 0.342 0.071 0.208 -0.126 N
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
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
non-normal distribution in transition periods
Lilliefors test for goodness of fit to a normal distributionat 5% significance level
surficial soil moisture variabilityDuring 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.
surficial soil moisture variabilityDuring 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
surficial soil moisture variabilityDuring 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
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
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
RSRS
R A
RA
T0 m
T0m
R X
RX T
C
TC
T S
TS (z,t)
v(x,y,t)
*
0
lnm
u zu zk z
Model+Earth Observation
+
20 July 200424 Oct. 2004
soil, vegetation, and landscape characterization through satellite images
Image on 18 June 2004
LAI ETp(mm/d)
ETp (mm/d)
image on 20 July 2004
LAI
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
KEY TO PROGRESS