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

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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