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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.

376 Environ Monit Assess (2008) 145:375386


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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

Environ Monit Assess (2008) 145:375386 377


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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



11/12

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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