metodlogia lluvia
TRANSCRIPT
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Optimal extension of the rain gauge monitoring network
of the Apulian Regional Consortium for Crop ProtectionE. Barca & G. Passarella & V. Uricchio
Received: 10 May 2007 / Accepted: 30 October 2007 / Published online: 23 November 2007# Springer Science + Business Media B.V. 2007
Abstract The goal of this paper is to provide a
methodology for assessing the optimal localization of
new monitoring stations within an existing rain gauge
monitoring network. The methodology presented,
which uses geostatistics and probabilistic techniques
(simulated annealing) combined with GIS instru-
ments, could be extremely useful in any area where
an extension of whatever existing environmental
monitoring network is planned. The methodology
has been applied to the design of an extension to a
rainfall monitoring network in the Apulia region(South Italy). The considered monitoring network is
managed by the Apulian Regional Consortium for
Crop Protection (ARCCP), and, currently consists of
45 gauging stations distributed over the regional
territory, mainly located on the basis of administrative
needs. Fifty new stations have been added to the
existing monitoring network, split in two groups: 15
fixed and 35 mobile stations. Two different methods
were applied and tested: the Minimization of the
Mean of Shortest Distances method (MMSD) and
Ordinary Kriging (OK) whose related objectivefunction is estimation variance. The MMSD, being a
purely geometric method, produced a spatially uni-
form configuration of the gauging stations. On the
contrary, the approach based on the minimization of
the average of the kriging estimation variances,
produced a less regular configuration, though a more
reliable one from a spatial standpoint. Nevertheless,
the MMSD approach was chosen, since the ARCCPs
intention was to create a new monitoring network
characterized by uniform spatial distribution through-
out the regional territory. This was the most important
constraint given to the project by the ARCCP, whosemain objective was to accomplish a territorial network
capable of detecting hazardous events quickly. A
seasonal aggregation of the available rainfall data was
considered. The choice of the temporal aggregation in
quarterly averages allowed four different optimal
configurations to be determined per season. The
overlapping of the four configurations allowed a
number of new station locations, which tended to
remain fixed season after season, to be identified.
Other stations, instead, changed their coordinates
considerably over the four seasons. Constraints weredefined in order to avoid placing new monitoring
locations either near existing stations, belonging to
other Agencies, or near the coast line.
Keywords Monitoring . Rain gauges .
Computational statistics . Simulated annealing .
Geostatistics . GIS
Environ Monit Assess (2008) 145:375386
DOI 10.1007/s10661-007-0046-z
E. Barca: G. Passarella (*) : V. Uricchio
Water Research Institute, CNR,
V.le De Blasio, 5,
70123 Bari, Italy
e-mail: [email protected]
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Introduction
With the growth of public environmental awareness
and the contemporary improvement in national and
EU legislation regarding the environment, monitoring
has assumed great importance in the frame of all those
managerial activities related to monitoring and safe-guarding the environment. In particular, over the last
decade, a number of public agencies whose purpose is
to monitor meteorological, hydrological and hydro-
geological parameters etc., have invested great eco-
nomic, technical and human resources in planning
and operating improvements on existing monitoring
networks within their catchment areas.
The problem of extending an environmental mon-
itoring network (EMN) has consequently increased its
importance in scientific literature because of the need
to produce reliable managerial tools (Arbia and Espa2001; Bogardi et al. 1985; Carrera and Szidarovzsky
1985; Cox and Cox 1994; Fedorov and Hackl 1994;
Harmancioglu et al. 1999; Knopman and Voss 1989;
Meyer et al. 1994; Nunes et al. 2002; Van Groenigen
and Stein 1998; Wu 2004).
Meteorological monitoring networks and particu-
larly those devoted to rainfall monitoring, are among
those which have received most attention from
researchers, with a consequent abundance in the
production of scientific papers, undoubtedly due to
the importance of this resource (Al-Zahrani and Husain1998; Bastin et al. 1984; Bras and Rodrguez-Iturbe
1975; Bras and Rodrguez-Iturbe 1976; Goovaerts
2000; Lebel et al. 1987; Papamichail and Metaxa
1996; Rodrguez-Iturbe and Meja 1974).
In particular, in the scientific community, the
problem of the extension of rainfall monitoring
networks has been tackled by searching for optimal
criteria for the positioning of new measuring gauges.
However, the sparse spatial coverage of regional
territories, and/or the technological obsolescence of
the gauges already installed, often provides scarceinformation on which to base reliable decision
processes.
Recent scientific literature has provided various
approaches, characterized by different levels of
complexity according to the level of detail required,
capable of supporting both the design and realization
of such networks (Ashraf et al. 1996).
The methodology proposed in this paper integrates
processes of stochastic and geostatistical theory with
optimisation methods based on simulation tools
(Pardo-Igzquiza 1998).
The methodology was applied to the design of an
extension to a rainfall monitoring network doubling
the number of the gauging stations. The considered
monitoring network is managed by the Apulian
Regional Consortium for Crop Protection (ARCCP)and it currently consists of 45 gauging stations
distributed randomly over the regional territory of
Apulia (South Italy) mainly as a result of administra-
tive needs.
The Apulian territory is also covered by a second
and denser network (about 150 stations), managed by
the Hydrographic Regional Office (HRO). Obviously,
the institutional aims of the two networks differ,
nevertheless, from a general managerial and economic
perspective, it is desirable that the monitoring
locations designed to widen the existing ARCCPnetwork should not overlap those belonging to the
concurrent network. The methodology needs, there-
fore, to be sufficiently flexible to exclude a new
proposed position, if the location is already covered
by a gauge belonging to the second network. In
general, the methodology allows buffer zones having
a different amplitude to be defined, where new
monitoring sites (e.g.: the coastal area) are not
needed.
Methodology
The proposed methodology consists of two steps; in
the first it is necessary to define an objective function
to be minimized. There are two possible choices: the
Minimization of the Mean of Shortest Distances
method (MMSD) and Ordinary Kriging (OK) whose
related objective function is the estimation variance.
The MMSD criterion was defined by Van Groenigen
and Stein (1998) and modified, to take into account
secondary information (weights), (Van Groenigen et al.2000). This criterion, as inferred by its definition, is
independent of the measured values and entirely based
on the relative position of the considered points;
therefore it is a geometric criterion and it provides
extremely regular final configurations. In the modified
version (Van Groenigen et al. 2000), it is possible to
introduce weights, conditioning the related objective
function, in order to define areas with a greater or
lesser need for monitoring sites.
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The OK criterion, derived from the theory of region-
alized variables (Matheron 1970), allows the value of
the estimation variance to be calculated in every location
of the new configuration (Journel and Huijbrechts
1978; Isaaks and Srivastava 1989; Goovaerts 1997).
In this case it is possible to define as the objective
function, the average or the maximum estimationvariance.
Also in this case, the estimation variances depend
uniquely on the sampling configuration; nevertheless a
variogram model needs to be defined (Journel and
Huijbrechts 1978; Isaaks and Srivastava1989; Goovaerts
1997) that implicitly models the spatial behaviour of
the considered variable. This phase is usually defined
as structural analysis.
The choice between the MMSD and OK optimisa-
tion approach cannot be based on a stringent
theoretical and quantitative criterion. MMSD, beinga geometrical driven method, produces spatially even
distributions of monitoring point locations, while OK,
based on estimation variance minimization, produces
a monitoring network capable of providing better
estimations in non-sampled points. In short, the first
method appears to be more useful for designing alert
monitoring networks, while the second is necessary
when a reliable statistic description of a spatial
phenomenon is required.
The second step of the proposed methodology
consists in the application of so-called simulatedannealing, which provides a number of random
configurations driven by the objective function. This
method, implemented by Deutsch and Journel (1992), is
used for finding the optimum in combinatorial prob-
lems, when the optimal solution of a given problem
needs to be selected among a large number of possible
available solutions without exploring them all.
The theory of simulated annealing is based on
the analogy with the organization of the atom network
of a metal when it undergoes a process of temperature
change (abrupt heating and slow cooling). Followingthis process, the atoms of the metal change their
arrangement to a configuration of low energetic
maintenance cost. In the analogy, the configuration
of the atoms corresponds to that of the sampling
points while the objective function corresponds to the
energy of the system (Pardo-Igzquiza 1998, Deutsch
and Cockerham 1994).
In algorithmic terms, with reference to the de-
scribed metallurgical analogy (Metropolis et al.
1953), we assign an initial value to the temperature
of the system, then we randomly choose a starting
configuration from all the possible configurations,
and we determine the corresponding value of the
objective function, which is called energy. The
temperature drives the duration of the process and,
at every following step it decreases down to zero,which is the final temperature; the slower the cooling
the higher the probability of finding the optimal
configuration is, while the greater the initial temper-
ature, the higher is the probability that the final
configuration matches the absolute optimum that is
the absolute minimum for the objective function.
The starting configuration is perturbed in a rando-
mised way, varying the position of only one sampling
point of the monitoring network at a time, and the
corresponding value of the objective function is
computed again. If the perturbed configuration is betterthan the previous one (i.e.: the value of the objective
function decreases) it is assumed as a transitory
excellent solution; otherwise, the new configuration
is not automatically discharged, as would happen with
a classical method of optimisation, but it is submitted
to a probabilistic criterion of acceptance which
compares it again with the transitory optimal config-
uration. If this probabilistic criterion establishes that
the configuration is acceptable, it is accepted as a
transitory optimal solution. In detail, this happens by
verifying that the following expression:
exp E
Ti
1
where E represents the variation of the objective
function, and Ti the current value of the temperature
parameter, is smaller than a randomly generated
number. This test allows the method to avoid the
process of converging to a local optimum rather than
the global one.
Independently from the criterion chosen, anotherspecific requirement was considered for improving
the monitoring network. In fact, once the results had
been obtained from one of the two methods, the
option of determining a number of mobile stations,
among the new monitoring locations was investigat-
ed. This option would allow the stations to be moved
within a given distance, during the seasons of the
year. The main reason why this option was investi-
gated is that a fixed monitoring network may
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sometimes be considered too rigid by agency manag-
ers, who ask for a certain flexibility in the gauge
positioning. Therefore, the methodology was repeat-
ed, considering seasonal rainfall means for the OK,
and the four best realizations for the MMSD criterion,
thus four different configurations were determined.
Successively, a possible maximum tolerance of10 km was introduced. In practice, a regular mesh of
10 km side cells was overlaid over the study area map
and the locations of the new gauging stations were
associated to the correspondent mesh cell by means of
GIS software (ESRI 1996). As a result, i t was
possible to distinguish two types of stations: those
whose position remained fixed in the same mesh cell,
throughout the four seasons and those whose position
changed. The new gauging stations belonging to the
former group, i.e., those which did not move from
their original mesh cell even when a reduction of the position tolerance to 5 km was considered, were
defined fixed stations. On the contrary, those
stations which moved from one cell to another during
the different monitoring seasons were labelled as
mobile stations. All the remaining stations were
defined as potentially mobile stations which means
that, even considering these stations as fixed stations,
it would be possible to choose some mobile stations
among them, to be moved to some other location in
particular seasons and conditions.
Study case
The methodology was applied to the design of an
extension to the rainfall monitoring network located
in the Apulian region. The monitoring network
considered is managed by the Apulian Regional
Consortium for Crop Protection (ARCCP) and cur-rently consists of 45 stations irregularly spread over
the regional territory. A second meteorological mon-
itoring network exists covering the area considered,
managed by the Hydrographic Regional Office and
consisting of about 150 stations. Figure 1 shows the
location of all the existing stations belonging to the
two networks, including also some stations outside
the regional boundaries but belonging to inter-
regional hydrographic basins. As stated above, even
though the two networks have different institutional
goals, a design constraint given by the ARCCP wasthat the monitoring locations should not overlap those
belonging to the concurrent network. Nevertheless, in
the present study, measurements from the HRO
stations were used to improve our knowledge of the
spatial behaviour of the mean seasonal rainfall, but
their locations were constrained so as not to allow
points in the optimisation algorithm. Other constraints
were defined related to the distance of new monitor-
ing points from both the existing ARCCP stations and
the coast line.
Fig. 1 Monitoring network
of the Apulian Regional
Consortium for Crop Pro-
tection and the Regional
Hydrographic Office. Sta-
tions lying outside the Apu-
lian boundaries belong to
interregional catchments
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The simulations were performed by using the
software SANOS (Van Groenigen and Stein 2000;
Van Groenigen 2000).
It is an established fact that any spatial analysis is
strongly dependent on the available data. In the study
case, they were taken from the electronic files of the
two regional agencies. As already mentioned previ-ously, the data were preliminarily submitted to a
statistical exploratory analysis. In particular, for every
station, rainfall data were aggregated per season. In
the following table the percentages of stations
belonging to each of the five Apulian provinces are
reported.
As Table 1 clearly shows, an equal number of
stations has been allocated to each province, provid-
ing an almost uniform distribution from an adminis-
trative standpoint. However, this distribution does not
guarantee spatial uniformity, since the provinces varyin size. It was therefore decided to plan design
simulations in order to re-equilibrate the coverage
percentages throughout the regional territory, favour-
ing those provinces having a worse gauge/km2 ratio.
ARCCP provided a series of daily rainfall data
related to the period 20002003. Obviously, a 3 year
temporal series is not enough to get representative
seasonal average values. Consequently, in order to
obtain the best possible characterisation of the spatial
behaviour of rainfall over the regional territory, a
decision was taken to use the historical seriesprovided by the HRO. This choice, however, did not
affect the correctness of the application of the
methodology; in fact, these data were used only to
determine the spatial law characterizing the mean
seasonal rainfall rate throughout the Apulian territory.
The precision in determining the spatial law depends,
obviously, on the abundance of the available data
throughout the territory. Thus, the historical series
published by the HRO were used only to appraise the
spatial behaviour of the mean seasonal rainfall, but
were ignored during the actual optimisation phase,
when, instead, only the positions of the existing
ARCCP gauging stations were considered.The historical series provided by the HRO cover
about a 50 year period, approximately from the 1950s
to today. This interval is long enough to define the
quarterly mean behaviour of rainfall, filtering possible
distortions due to intense phenomena and, in partic-
ular, rainy or dry periods.
As stated above, there are about 150 monitoring
stations belonging to the HRO network, but 27 of
them are located in the provinces of Potenza and
Avellino (Fig. 1), outside the Apulian borders, for
monitoring the inter-regional basins of the riversOfanto, Candelaro and Carapelle.
Unfortunately, for various reasons, only 93 gaug-
ing stations were actually usable for the simulations,
instead of 150, corresponding to a spatial density of
about 0.005 stations per squared kilometre.
Using the aggregated values of these stations, some
preliminary, descriptive statistics were computed in
order to evaluate the related PDFs. In fact, it is
preferable that these distributions should be normal to
respect the ordinary kriging hypotheses. Table 2
reports the main descriptive statistics for each periodof 3 months.
Table 1 Density of rain gauging stations, in each province, of
the Apulian Regional Consortium for Crop Protection
Province Area (km2) No. of
gauging
stations
Density
(stations/km2)
Density
(%)
Bari 5,127,609 9 0.00176 11.83
Brindisi 1,843,752 9 0.00488 32.91
Foggia 7,157,063 9 0.00126 8.48
Lecce 2,770,077 9 0.00325 21.90
Taranto 2,438,445 9 0.00369 24.88
Apulia 19,336,946 45
Table 2 Descriptive statistics of precipitation (mm) in the four
quarters of the year
Quart 1 Quart 2 Quart 3 Quart 4
No. of cases 93 93 93 93
Minimum 38.0 23.4 22.6 51.2
Maximum 94.7 66.4 59.2 119.0
Range 56.7 43.0 36.6 67.8
Median 63.3 36.8 33.8 73.8
Mean 63.8 39.7 35.1 77.9
95% CI. sup. 66.1 41.6 36.4 81.2
95% CI. inf. 61.5 37.7 33.8 74.6
Std. error 1.2 1.0 0.7 1.7
Standard dev. 11.2 9.6 6.3 16.2
Variance 126.0 92.7 39.3 262.2
C.V. 0.2 0.2 0.2 0.2
Skewness 0.4 0.7 0.9 0.7
Kurtosis 0.1 0.2 1.7 0.2
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Table 2 shows that the values of the mean and the
median related to each quarter are almost the same,
pointing out a tendency to symmetry of the sample
distributions (Ott1995). In fact, since the values used
are quarterly means of daily values, unless phenomena
of casual or systematic distortion affect the starting
values, the distributions are expected to be normal, because of the Central Limit Theorem. The normal
distribution of data is at the basis of the geostatistical
approach (ordinary kriging); consequently, the statisti-
cal analysis described below was functional to the
verification of this hypothesis, with the purpose of
guaranteeing non-distorted results when applying the
ordinary kriging, rather than the MMSD, method.
The non-parametric KolmogorovSmirnov (KS)
test, with a 99% level of significance (Massey 1951;
Lilliefors 1969), was applied to all the seasonal data
of all the gauging stations, outlining the followingresults: in all the seasons nothing suggests the sample
distributions are non-normal, or better, no meaningful
differences were shown among the Gaussian distribu-
tion and the four sample distributions. The box and
whiskers and the stem and leaf diagrams confirmed,
even though at a qualitative level, that the frequency
distributions for each of the considered seasons are
approximately symmetrical.
Figure 2 shows an overview of the four box and
whiskers diagrams of every season and allows the
shape and the position of the four distributions to be
compared. These diagrams represent schematically
the main characteristics of the distributions. In
particular, the box represents the first and third
quartiles and the median, while the whiskers
represent the range between the first and the 99th
percentile; outliers, outside this range, are alsomarked.
Observing the diagrams in Fig. 2, it appears that,
during the summer, the median is smaller than in
autumn and winter, confirming that it rains less in
warm seasons. A wide dispersion of rainfall values is,
finally, evident around the median during the rainiest
seasons, which is symptomatic of a great non-
homogeneity of the phenomenon over the territory.
All this information, jointly with the results of the
normality test, confirms the hypotheses made about
the average behaviour of the considered phenomenon,which was, partially, already known.
Following the preliminary phase of statistical
investigation, the experimental variograms, represent-
ing the spatial behaviour of the mean seasonal rainfall
rate for each season, were calculated and the
theoretical models were determined. In the following
Table 3, the parameters of the four theoretical vario-
grams are reported; all of them were spherical and
anisotropic. The reported ranges are those related to
the principal axis of anisotropy.
Fig. 2 Box and whiskers
diagrams of the average
rainfall values recorded at
the 93 considered gauging
stations of the Hydrographic
Regional Office
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From a comparison of the values in Table 3 it can
be seen that:
During the cold seasons (I and IV quarter) the scale
of the considered phenomenon is wider; this
indicates that the differences in rainfall values for
different areas of the region are greater; this is most
evident observing the dispersion of values around
the median in the box and whiskers diagrams;
Likewise, during these seasons the discontinuity
at the origin (nugget) increases; this indicates that
the rainfall values tend to differ even over short
distances. This can be explained by spatial
discontinuity, which is a specific peculiarity of
rainfall phenomena.
Similar values of the ranges point out, instead,
that the distance of the spatial correlation remains
nearly constant, around 65 km, throughout the
four seasons;
Throughout all the seasons there is a strong
anisotropy (ratio from 2 to 5) of the phenomenon
with a principal direction more or less parallel to
the coast line.
The proposed methodology was applied and four
different configurations were determined both for the
OK and the MMSD criteria. As expected, the
configurations produced by the two approaches are
notably different. In fact, while for OK, the optimi-
sation process uses seasonal variograms, the MMSD
criterion involves only the locations of the existing 45monitoring stations. Consequently, while the four
configurations obtained by OK are really seasonal
configurations, the four obtained by MMSD are simply
the best four among several realizations. However, for
a matter of clarity, in both the cases the four config-
urations have been labelled as seasonal.
Figure 3 shows, as an example, the simulated
configurations for the first season, using as objective
function the average of the OK estimation variances
(Fig. 3a) and the average of the distances between an
arbitrary point and its nearest neighbour (Fig. 3b);
white circles indicate the new monitoring station
locations. The grey area along the coast and the inner
borders represents the part of the regional territory
where the algorithm was constrained to avoid the
placement of new monitoring points.Obviously, the second approach, being purely
geometric, produced a new configuration, with a very
regular distribution of the gauging stations. On the
contrary, the approach based on the minimization of
the average of the kriging estimation variances,
produced a less regular configuration, but, more
reliable from a spatial standpoint, in terms of
estimation variance.
The managers of the ARCCP preferred the
approach based on the minimization of the average
distance among the points since it allowed the spatialdensity of the gauging stations to be made consistent
at a provincial level. Thus, the MMSD approach was
chosen as the working criterion, simply on the basis
of managerial requirements.
The ARCCP also asked for the 50 new stations to
be set up, divided into two groups: 15 fixed and 35
mobile stations. This request can be explained, in
managerial terms, by the necessity of getting more
detailed information from different parts of the
Apulian territory according to the current season,
with the purpose of defining and circulating reliableforecasts of rainfall availability, among the pooled
consumers. The overlapping of the four seasonal
configurations allowed a number of new station
locations to be located automatically, which tended
to remain unchanged season after season and certain
others that, on the contrary, sometimes changed their
coordinates considerably.
The methodology, as described above, yielded the
required number of locations where the new gauging
stations could be placed. Nevertheless, this result was
considered too rigid by the managers of the ARCCP,and they asked for a certain flexibility in the gauge
positioning, since some locations were not achievable.
Consequently, a possible maximum tolerance of
10 km was introduced between the determined and
actual gauge position. In practice, a regular mesh of
10 km side cells was overlaid on the Apulia map and
the locations of the 50 new gauging stations were
associated to the correspondent mesh cell by means of
GIS software (ESRI 1996). Doing so, it was possible
Table 3 Characteristic parameters of the theoretical variograms
for the four seasons
Nugget
(mm2)
Sill
(mm2)
Range
(m)
Anisotropy
angle (deg)
Anisotropy
ratio
Quart 1 40 130 65,000 150 2.5
Quart 2 10 80 65,000 150 3.3
Quart 3 5 60 70,000 150 5.0
Quart 4 40 200 65,000 150 2.0
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to distinguish two types of stations: those whose position remained fixed in the same mesh cell,
throughout the four seasons and those whose position
changed. Forty-two of the 50 new gauging stations
belonged to the first group, and at least 17 of them
were kept strictly to their original position, allowing a
reduction of the position tolerance to 5 km.
Only eight stations moved from one cell to another
during the different monitoring seasons. These eight
gauging stations were labelled as mobile stations,
while the previous 17 were, obviously, considered
fixed stations. The remaining 25 stations were definedas potentially mobile stations which means that even
considering these stations as fixed stations, it would
be possible to choose some mobile stations among
them, to be moved to some other location in particular
seasons and conditions.
Figures 4 and 5 show graphically what was said
above. In particular, Fig. 4 summarizes the four
seasonal configurations; the squared boxes represent
those cases where the gauging stations remain almost
fixed throughout the year. Figure 5 shows the four
final configurations of the new gauging stations perseason, labelled according to type, achieved by means
of the minimization of the mean of shortest distances
method (MMSD). Finally, Table 4 reports the number
of stations of the upgraded monitoring network per
Province. The last column of Table 4 shows that the
percent density of stations per Province has been re-
balanced as required by the ARCCP.
Conclusion
One of the main institutional assignments of the
Regional Consortium for Crop Protection is the
elaboration of data gained from the meteorological
monitoring network with the purpose of obtaining
information about the state of crops, hazards related to
the actual and predicted meteorological conditions,
and advising on agricultural practices to safeguard
agricultural production and the environment.
The precision of the predicted information and the
climatological characterization of the territory arestrongly conditioned by the optimal spatial arrange-
ment of the monitoring stations. The present study
holds particular importance also because it aims to
improve the efficiency and effectiveness of the whole
agro-meteorological monitoring system.
The broadening of the agro-meteorological moni-
toring network is aimed at achieving more and more
precise and reliable predictive information, able to
satisfy the increasing need of knowledge regarding
meteorological and climatic phenomena.
An optimal location of the monitoring stations wasestablished through the application of geostatistical
methodologies, which followed a climatic character-
ization of the region based on a time series analysis of
available data. In fact, the characterization of any
spatial or timespace phenomena represents the first
and most important step of any geostatistical study.
Geostatistics methods, including kriging and cokrig-
Fig. 3 Configurations of the new monitoring network of the Apulian Regional Consortium for Crop Protection resulting for the first
season using a the average of estimated variances of ordinary kriging; b the average of the points distance from those at the border line
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ing techniques, were used to finalize the estimation of
spatial variables that is intrinsically linked to the
territory. In particular, kriging and its modifications
besides providing an estimation of the considered
spatial variable, also gives a measure of the precision
of the estimation in terms of estimation variance.
The proposed study highlighted, qualitatively and
quantitatively, the variability of the considered phe-
nomenon, specifying its typology, with regard to thepresence of possible anisotropies and to the existence
of different space or time scales of variability.
A first phase of statistic analysis, on a quarterly
level data, was followed by the computation of the
experimental variograms and the variogram model
fitting; the analysis of these variogram models
(spherical), clearly highlighted some peculiar charac-
teristics of the Apulian climate, consisting in a great
spatial variability of rainfall during the Winter, even
over relatively small distances with a constant spatial
correlation distance of about 65 km. This character-
istic of seasonal variability was also confirmed by
other approaches, including a qualitative analysis
carried out by means of GIS instruments.
Comparing the results obtained, it was possible to
define the co-ordinates of the optimal locations wherethe 50 new monitoring stations should be placed.
Subsequently, a distinction was made between fixed
and mobile stations: those, among the 50 new
stations, characterized by a strong convergence of
the seasonal optimal locations were classified as
fixed. The results of the present study were put into
practice with the actual setting of the 50 monitoring
Fig. 4 Final configuration from the elaborations in the four quarters of a year
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stations in the suggested locations. This practical
evolution of the methodology gives it an added value
related to the possibility of continuously checking the
efficiency of the proposed solution. Moreover, it
allows further experiments to be made aimed at
improving the methodology itself.One of the critical aspects of the proposed
methodology is the choice between the MMSD and
OK optimisation approach. Nevertheless, in our
opinion it cannot be based on a stringent theoretical
and quantitative criterion. In fact, MMSD is a
geometrically driven method, and consequently pro-
duces spatially even distributions of monitoring point
locations. OK, instead, is based on estimation
variance minimization and produces a monitoring
network capable of providing better estimations in
non-sampled points. In short, the former method ismore useful for designing alert monitoring net-
works, while the second is necessary when a reliable
statistical description of a spatial phenomenon is
required. A further planned improvement of the
methodology consists in adopting a combined or
mixed two- or multiple-step approach, which integra-
tes the two approaches, so that the drawbacks of one
method are partly compensated by the advantages of
the other one.
A brand new feature of the proposed methodology
consists in the possibility of designing a flexible
monitoring network. Providing a criterion for distin-
guishing between mobile and fixed gauging
stations allows the monitoring administrator to change
the network configuration over a period of time,taking into account the seasonal behaviour of the
considered natural phenomenon.
Finally, a simplification was made with regard to
installation costs. A total of 50 new stations was
adopted in this paper, neglecting the trade-off between
cost and accuracy of results, since the given budget
Fig. 5 Results from the
elaborations for the 4 year
seasons and groupings
Table 4 New density of rain gauging stations, in each
province, of the Apulian Regional Consortium for Crop
Protection
Province Area
(km2)
N
stations
Density
(stations/km2
)
Density
(%)
Bari 5,127,609 25 0.00488 19.52
Brindisi 1,843,752 10 0.00542 21.51
Foggia 7,157,063 34 0.00475 19.12
Lecce 2,770,077 14 0.00505 20.32
Taranto 2,438,445 12 0.00492 19.52
Puglia 19,336,946 95
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available for improving the monitoring network was
already defined by the ARCCP. Nevertheless, a
further development of the methodology could be
the possibility of introducing a satisfactory balance
between costs and results, allowing a reliability
threshold to be defined in terms of either monitoring
station density in the MMSD case, or estimationvariance in the OK case.
Acknowledgements The authors wish to acknowledge the
courtesy of the Apulian Regional Consortium for Crop Protection
(ARCCP) and Hydrographic Regional Office (HRO) in providing
data used throughout the paper.
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