GIT – Geology and Information Technology

GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Stima dell'infiltrazione efficace nell'area alpina ed
appenninica tramite dati aperti e software open
source
Mauro Rossi [1,2], Marco Donnini [1,2], Francesco Frondini [2], Fausto Guzzetti [1]
1
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, Perugia, Italy
2 Università degli Studi di Perugia. Dipartimento di Fisica e Geologia. Perugia, Italy
Montefalco (PG), 16 - 19 giugno 2014
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
The question
What is effective infiltration?
The infiltration is one
of the main processes
that regulates the
water cycle
THE WATER
CYCLE
Effective Infiltration
(EI):
amount of meteoric
water (mm/year)
that infiltrates into the
subsoil
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Estimation methods
How can we estimate effective infiltration (EI)?
 Locally: using infiltrometers (in field)
 Over large areas: using direct or indirect methods
 Direct method
Ratio between the volume of water discharged on average by the springs (Q) and
their recharge area (A).
EI = Q/A
Central Italy (Boni and Bono 1982, Boni et al. 1986, Mastrorillo et al. 2009, Mastrorillo and
Petitta 2010)
 Indirect methods: empirical coefficients (EIC = Effective Infiltration Coefficients).
EI may be determined in terms of percentage of infiltration of actual precipitation,
depending on the outcropping lithotypes.
(Drogue 1971, McCuen 1989, Wanielista 1990, Bonacci and Magdalenić 1993, Savenije 1996,
Wu et al. 1996, Bonacci 1999 and 2001, Civita 2005, Mastrorillo et al., 2009)
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Some problems
Which problems occur in the EI estimation?
 Infiltrometers measures are rare and often they don’t represent accurately
the spatial variation of infiltration processes.
 In direct method the accuracy when defining the boundaries of the recharge
areas, as well as the availability of the spring discharges data, are critical.
 The results of the indirect methods are biased by the fact that the
classification of recharge areas is solely based on mean lithological
parameters in terms of permeability.
Additionally, the effectiveness of these methods depends on the reliability of
the temperature-precipitation parameters that are rare or altogether missing
in mountains areas.
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
New approach
Description of the model
The model here presented is based on a
public domain software written in Java
by McCabe and Markstrom (2007) from
USGS.
The code performs a monthly water
balance analysis based on methods
proposed by Thornthwaite (1948) and
Mather (1978, 1979).
The original code was modified to be (1)
spatially distribuited, (2) to consider deep
infiltration, (3) to estimate the results
variability and (4) to use open data.
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Open source approach
Open source software and open data
The model was developed using open
source software and open data.
The origina Java code was rewritted in
R integrating in input different open
data relative to precipitation,
temperature, soil parameters and
elevation.
Preparing
input data
Original code
Several spatial data were elaborated
using GRASS and QGIS.
Model engine
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Model inputs
Which data are used in the model?
Climatic data. World mean monthly temperature and monthly total
precipitation values, from the Intergovernmental Panel on Climate
Change (IPCC) website (0.5° grid resolution)
Surface elevation data. World digital elevation model ASTER GDEM,
from the Earth Remote Sensing Data Analysis Center (ERSDAC) of Japan
and NASA's Land Processes Distributed Active Center (LP DAAC) website
(30 m grid resolution)
Soil/Environmental data. Eurasian soil thematic data of the European
Soil Database (v2.0), from the Joint Research Center (JRC) website
(10 km grid resolution)
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Model inputs
Which data are used in the model?
The balance computation was performed into a
Climatic data. World mean monthly temperature and monthly total
0.5°
gridfrom
(coincident
to the IPCC Panel
grid).on Climate
precipitation
values,
the Intergovernmental
Change (IPCC) website (0.5° grid resolution)
Surface elevation data. World digital elevation model ASTER GDEM,
from the Earth Remote Sensing Data Analysis Center (ERSDAC) of Japan
and NASA's Land Processes Distributed Active Center (LP DAAC) website
(30 m grid resolution)
Soil/Environmental data. Eurasian soil thematic data of the European
Soil Database (v2.0), from the Joint Research Center (JRC) website
(10 km grid resolution)
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Meteorological data
IPCC Network
Mean monthly temperature and
precipitation from the IPCC Climate
Research Unit (CRU) high resolution
climate database were used in the model.
Global data in the period 1961 – 1990 at
0.5° grid resolution.
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Meteorological data
IPCC Network
Mean monthly temperature and
precipitation from the IPCC Climate
Research Unit (CRU) high resolution
climate database were used in the model.
Global data in the period 1961 – 1990 at
0.5° grid resolution.
Input data variability was integrated in the model:
Pmin = P – (UPmin × P)
Pmax = P + (UPmax × P)
UPmin = 0.75 and UPmax = 1
Tmin = T – UTmin
Tmax = T + UTmax
UTmin = 5°C and UTmax = 5°C
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Elevation data
ASTER Global Digital Elevation Model
World digital elevation model ASTER GDEM was
used in the model.
The computation was performed using 0.5° grid
resolution (IPCC grid).
For each cell the minimum, maximum and
mean elevation value was calculated.
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Water holding capacity
JRC European Soil Database (ESDB) data
Water holding capacity (WHC) was
derived from different layers of the JRC
(10 km grid resolution)
 Subsoil water capacity (AWC_SUB)
 Topsoil water capacity (AWC_TOP)
 Depth to rock (DR)
Using the following equation:
WCH = [ AWC_SUB × DR/2 ] + [ AWC_TOP × DR/2 ]
WHC, AWC_SUB, AWC_TOP = mm/m; DR = m
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Water holding capacity
JRC European Soil Database (ESDB) data
For each cell of the IPCC
grid (0.5°) we computed the
minimum, the maximum
and the mean values of
WHC
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Runoff and direct runoff factor
JRC European Soil Database (ESDB) data
Direct runoff factor (drofrac) is the fraction of rainfall
(Prain) that becomes direct runoff (DRO) in a month
(Mccabe and Markstrom, 2007)
DRO = Prain × drofrac
The runoff factor (rfactor) parameter is the fraction of
surplus (S) that becomes runoff (RO) in a month
RO = S × rfactor
We let drofrac and rfactor vary according to the
dominant surface textural class (TEXT_SRF_DOM).
For each cell of the 0.5° grid was computed the minimum, the
maximum and the mean value of drofrac.
Introduction
Materials and methods
Study area (Apennines)
Description
drofrac
rfactor
NA
NA
NA
Non soils
0.025
0.350
No mineral texture (peat soils)
0.050
0.100
Coarse
0.010
0.500
Medium
0.025
0.450
Medium Fine
0.040
0.350
Fine
0.050
0.250
Very Fine
0.060
0.100
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Elevation/Climatic - depending parameters
ASTER GDEM – IPCC data
The first computation of the water-balance model is the estimation of the
amount P that is rain (Prain) or snow (Psnow), in millimetres.
When T is below a specified threshold (Tsnow), all precipitation is considered to be snow.
If T is greater than an additional threshold (Train), then all precipitation is considered to be rain.
Psnow = P × [ ( Train – T ) / ( Train – Tsnow ) ]
Prain then is computed as:
Prain = P – Psnow
About Train we assumed for it the constant value of 3.3°C and for Tsnow we considered:
•
Tsnow = - 10°C for elevation of 0 m,
•
Tsnow = - 1°C for elevation higher than 1000 m
•
for elevation between 0 and 1000 m we computed Tsnow as:
Tsnow = Tsnow0 – (|Tsnow1000| - |Tsnow0|) × (elevation/1000)
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Elevation/Climatic - depending parameters
ASTER GDEM – IPCC data
Snow melt fraction (SMF) is another component of the water balance model and it is the
fraction of snow storage (snostor) that melts in a month, and is calculated as:
SMF = [ ( T – Tsonw ) / ( Train – Tsnow ) ] × meltmax
T is the mean monthly temperature and meltmax is the maximum melt rate. In this work:
•
meltmax = 1 for elevation of 0 m,
•
meltmax = 0.5 for elevation of 1000 m
• and for elevation between 0 and 1000 m is given by:
meltmax = meltmax0 + (meltmax1000 - meltmax0) / (elevation × 1000)
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Elevation/Climatic - depending parameters
ASTER GDEM – IPCC data
Actual evapotranspiration (AET) is derived from potential evapotranspiration (PET), Ptotal,
soil-mosture storage (ST) and soil-mosture storage withdrawal (STW).
PET is calculated by using the Hamon equation (Hamon, 1961):
PET Hamon = 13.97 × d × D2 × Wt
where PET Hamon is PET in millimeters per month, d is the number of days in a month, D is
the mean monthly hours of daylight in units of 12 hrs, and Wt is a saturated water vapor
density term, in grams per cubic meter, calculated by:
Wt = (4.95 × e0.062 × T ) / 100
where T is the mean monthly temperature in degrees Celsius (Hamon, 1961).
When Ptotal for a month is less then PET, then AET is equal to Ptotal plus the amount of soil
moisture that can be withdrawn from storage in the soil. Soil moisture storage withdrawal
(STW) linearly decreases with decreasing ST such that as the soil becomes drier, water
becomes more difficult to remove from the soil and less is avaiable for AET.
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Implementation of deep storage
JRC European Soil Database (ESDB) data
We introduced the new parameter deepstor.
It is the deep storage and is increased by water
in the soil that infiltrates in depth in a month
(PMHfact). It can be computed as:
• PMHfact = WHC × PMH (this analysis)
Description
• PMHfact = remain × PMH
PMH values (ranging from 0 to 1) were derived
from hydrogeological type of parental material
(PMH).
remain(t+1) = remain(t) - PMHfact
Introduction
Materials and methods
Study area (Apennines)
NA
PMH
NA
No information
0.7
Non soils
0.7
Porous - Stor. ~ Perm. +
0.9
Porous 2 Stor. ~ Perm. +
0.9
Porous 1 Stor. + Perm. +
0.9
Stor. - Perm. -
0.5
Hard. Stor. -- Perm. --
0.3
Soft. Stor. -- Perm. --
0.3
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Hydrological balance computation
In order to compute the mean, the minimum and the maximum value of
remain and deepstor, was considered different combinations on the
input data:
Parameter Scenario 1 (Min) Scenario 2 (Mean) Scenario 3 (Max)
T



P



WHC



Elevation



PMH



drofrac



DRO



Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Central Apennine
ID
Mean annual ID
EI (mm/y)
Mean annual
EI (mm/y)
The test area
S1
888
U4
183
S2
860
U6
281
U1
847
S5
803
G1
869
U7
245
U2
706
S6
888
U3
828
S7
365
G2
918
S8
568
S3
983
G5
533
S4
752
S9
637
G3
879
U8
778
G4
714
G6
280
U5
231
S10
290
(Boni et al., 1986)
(Crampon et al., 1996)
Introduction
Materials and methods
Carbonate rocks are a prominent feature and form important
aquifers in central and southern Italy. A series of volcanic
aquifers extend along the west side of the Apennines (Crampon
et al., 1996).
Boni and Bono (1982) and Boni et al. (1986) identified the main
hydrogeological structures of Central Italy and for each structure
evaluated EI (varing around 180 to 980 mm/year) knowing the
discharge from springs and the boundaries of the recharge areas.
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
The Alps
The new application area
(Donnini et al., 2013)
Roughly we could identify an inner crystalline
core surrounded by carbonate and flyshoid
complexes.
The crystalline - metamorphic rocks are not
very permeable except where fractures have
(Crampon et al., 1996)
been created by tectonism, while the main
The hydrogeology of the Alps is highly variegate springs are located in calcareous complexes.
because of its extremely complicated geology.
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Main results
Results obtained and their variability (in time and space)
Model results consists:
 Plots on monthly variation of model parameters,
 Yearly synthesis maps of model parameters (min,
max and mean values)
The main infiltration parameters are:
Effective Infiltration (EI)
EI = Deep Storage (mm/year)
Effective Infiltration Coefficient (EIC)
EIC = (Soil storage + Deep storage) / Year precipitation
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Main results
Results obtained and their variability (in time and space)
The computation performed with the
maximum values represents well the EI
values for the test area (Apennines):
 Boni et al., 1986:
EI min = 183 mm/year
EI max = 983 mm/year
EI mean = 638 mm/year
 This work:
EI min = 257 mm/year
EI max = 701 mm/year
(Boni et al., 1986)
EI mean = 427 mm/year
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Main results
Results obtained and their variability (in time and space)
The computation performed with the
maximum values represents well the EIC
values for the test area (Apennines):
EIC min = 0.19
EIC max = 0.5
EIC mean = 0.32
EIC from Civita (2005)
Hydrogeological
%
complex
Units
Carbonate rocks
50 - 100 0.5 - 1
Sandstone
5 - 25
0.05 - 2.5
Volcanic deposits
50 - 90
0.5 - 0.9
Intrusive rocks
15 - 35
0.15 - 0.35
Metamorphic rocks
5 - 20
0.05 -0. 2
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Conclusions
How this model could be useful
The values obtained for the test area (Apennines) are comparable but some differences
persist.
We think that these differences are due by:
 the resolution of input data,
 different methods on the EI enstimation.
Anyway we think that:
 the model here shown is useful for the estimation of the water balance components
at regional scale,
 the use of open data and open source software allow the application in different
areas of the Eurasian continent.
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions
GIT – Geology and Information Technology
9a Riunione del Gruppo di Geologia informatica - Sezione della Società Geologica Italiana
Montefalco (PG), 16 - 19 giugno 2014
Thank you for your attention
Introduction
Materials and methods
Study area (Apennines)
Study area (Alps)
Results
Conclusions

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