GIS and Geovisualization Technologies Applied to Rainfall Spatial
Patterns over the Iberian Peninsula using the Global Climate Monitor
Web Viewer
Juan Antonio Alfonso Gutiérrez, Mónica Aguilar-Alba and Juan Mariano Camarillo Naranjo
Departamento de Geografía Física y Análisis Geográfico Regional, Universidad de Sevilla, Spain
Keywords: Geovisualization, Precipitation, Climate Data, Spatial Databases, Iberian Peninsula, Spatial Analysis.
Abstract: Web-based GIS and geovisualization are increasingly expanding but still few examples exist with regard to
the diffusion of climatic data. The Global Climate Monitor (GCM) (
created by the Climate Research Group of the Department of Physical Geography of the University of
Seville was used to characterize the spatial distribution of precipitation in the Iberian Peninsula. The
concern about the high spatial-temporal variability of precipitation in Mediterranean environments is
accentuated in the Iberian Peninsula by its physiographic characteristics. However, despite its importance in
water resources management it has been scarcely addressed from a spatial perspective. Precipitation is
characterized by positive asymmetric frequency distributions so conventional statistical measures lose
representativeness. For this reason, a battery of robust and non-robust statistics of little used in the
characterization of precipitation has been calculated and evaluated quantitatively. The results show
important differences that might have significant consequences in the estimation and management of water
resources. The realization of this study has been carried out using Open Source technologies and has
implied the design and management of a spatial database. The results are mapped through a GIS and are
incorporated into a web geovisor ( in order to facilitate
access to them.
Current climate research benefits from the existence
of large and global climate databases are produced
by various international organizations. The common
denominator is the availability and accessibility
under the 'open data' paradigm. Very often, these
new datasets cover the entire earth at a more regular
spatial distribution (normally gridded), with a longer
and more homogeneous time span and are built
under more-robust procedures.
Many varied sources of information are at the
basis of global datasets that are accessible on the
reference web portals of the subject. It is important
to note the wide availability of these global datasets
and their quality. In most cases these datasets are
distributed under open-database licenses. This
distribution has favoured the increasingly
widespread use of global data by scientists, and the
emergence of countless references from studies
based on these data (Folland et al., 2001; Jones &
Moberg, 2003; New, Hulme & Jones, 2000, etc.).
However, the complexity of the very technical
formats of distribution (netCDF or huge plain text
files with millions of records) limits these datasets to
a very small number of users, almost exclusively
scientists. For non-expert users, it is important to
develop and offer new environmental tools in an
open and transparent manner because stakeholders,
users, policy makers, scientists and regulators prefer
it and demand it (Carslaw and Ropkins, 2012, Jones
et al., 2014).
These open data and open knowledge paradigm
also referred by some as 'the fourth paradigm'
(Edwards et al., 2011) responds, in relation to
climate data, to the double challenge that climate
science is currently facing; on the one hand, it has
to guarantee the availability of data to permit more
exploration and research and, secondly, it has to
reach citizens. This leads directly to the use of Open
Source technologies supported by an extensive
worldwide community of users that provide tested
evidence in very stressful applications. Such a large
Alfonso Gutiérrez, J., Aguilar-Alba, M. and Camarillo Naranjo, J.
GIS and Geovisualization Technologies Applied to Rainfall Spatial Patterns over the Iberian Peninsula using the Global Climate Monitor Web Viewer.
DOI: 10.5220/0006703200790087
In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 79-87
ISBN: 978-989-758-294-3
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
and proven implementation in results and experience
has also motivated the use of these open technologies
in our research. Particularly, the Climate Research
Group of the University of Seville has a remarkable
experience with PostgreSQL/PostGIS being the core
of the data management and research tools.
Concerning the geovisualization, it is worth
noting that it is a crucial element in applications for
decision support; web services are particularly
appropriate for this (Vitolo et al., 2015). Also, web-
mapping has become an effective tool for public
access to information in general and to climate
knowledge and climate monitoring in particular,
specially considering the ongoing advances in web
GIS and geovisualization technologies.
Despite the fact that there are some very
specialized geoviewers to access and download the
data (for example, the Global Climate Station
Summary by NCDC-NOAA) in most cases, these
viewers are very general and/or offer poor geo-
displays (European Climate Assesment). The Global
Climate Monitor (GCM) system belongs to this
field, more precisely to the field of researching
possibilities of dissemination of monitored climatic
information through the use of geospatial web
viewers. In this work, we focus on showing the
possibilities of the GCM in climatic research by
analysing the spatial distribution of precipitation in
the Iberian Peninsula located in southern Europe.
Understanding the spatial distribution of rainfall
is an important element for the management of
natural resources in the Iberian Peninsula. With the
exception of the northern mountain range, the
Iberian Peninsula is included in the Mediterranean
climate domain (De Castro et al., 2005; Martin-
Vide, 2011b) showing the inter-annual irregularity
characteristic of the this type of climate (García-
Barrón et al., 2011). Its main characteristics are
marked fluctuations from rainy years to periods of
drought together with an irregular intra-annual regime
with minimum values of rainfall during the summer
months (García-Barrón et al., 2013). Due to these
characteristics decision-making in water management
requires the delivery of accurate scientific information
that provides objective criteria for the technical
decisions in the water planning process directly
affecting the environment and society (Krysanova et
al., 2010; Cabello et al., 2015).
Furthermore, in order to advance the knowledge
of rainfall regime in the Iberian Peninsula,
researchers have related the annual, seasonal or
monthly volume to synoptic situations mainly linked
to patterns of atmospheric circulation and weather
types (Muñoz-Diaz and Rodrigo, 2006; López-
Bustins et al., 2008; Casado et al., 2010; Hidalgo-
Muñoz et al., 2011; Cortesi et al., 2014; Ríos-
Cornejo et al., 2015). Nevertheless, there are few
studies dedicated to the analysis of the spatial
variation of the rainfall for the Iberian Peninsula.
The present study introduces a complementary
aspect calculating a set of robust and non-robust
statistics for the characterization of precipitation.
Robust statistical measures are not commonly used
when characterizing neither precipitation nor for the
estimation of water resources in environmental
management and planning despite the positive
skewness of the frequency distributions. The
differences between both types of measurements
have also been obtained quantitatively in order to
evaluate the possible bias when estimating
precipitation volumes.
This work has involved the implementation of a
spatial database of high volume that would allow the
analysis and processing of data. Results can be
viewed using GIS technologies and this information
is disseminated and made accessible to final building
up a new geoviewer (
The Iberian Peninsula is located in the southwestern
end of Europe, next to North Africa, and, thereby
and surrounded by the Mediterranean Sea to the East
and by the Atlantic Ocean to the West. Due to
transition situation between the middle latitudes and
the subtropical ones, and to its complex orography,
its climatic characteristics and types are very diverse
due to the complex patterns of spatio-temporal
variability of most climatic variables (Garcia-Barrón
et al., 2017).
The GCM currently displayed corresponds to the
CRU TS3.21 version of the Climate Research Unit
(University of East Anglia) database, a product that
provides data at a spatial resolution of half a degree
in latitude and longitude, spanning from January
1901 to December 2012 on a monthly basis. From
January 2013, the datasets that feed the system are
the GHCN-CAMS temperature dataset and the
GPCC First Guess precipitation dataset (Global
Precipitation Climatology Centre) Deutscher
Wetterdienst in Germany.
The data that are currently offered in the display
come from three main datasets: the CRU TS3.21, the
GHCN-CAMS, and the GPCC first guess monthly
product. The basic features of these products are
shown in table 1.
GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management
Table 1: Basic features of the datasets included in the Global Climate Monitor.
Dataset CRU TS3.21 GHCN-CAMS GPCC first guess
0.5ºx0.5º lat by lon 0.5ºx0.5º lat by lon 1ºx1º lat by lon
Time span
January 1901 to
December 2012
January 1948 to
present expired month
August 2004 to
present expired month
Time scale Monthly Monthly Monthly
Format netCDF Grib netCDF
Total precipitation amount
Average mean temperature
Average mean temperature Total precipitation amount
Figure 1: Global Climate Monitor view.
The CRU TS3.21 dataset is a high-resolution
grid product that can be obtained through the British
Atmospheric Data Centre website or through the
Climatic Research Unit website. It has been
subjected to various quality controls and
homogenization processes (Mitchell & Jones, 2005).
It is important to note that this database is also
offered in the data section of their website and in
their global geovisualization service
(Intergovernmental Panel on Climate Change, 2014).
Apart from this organization, CRU TS is one of
the most widely used global climate databases for
research purposes. The GHCN and GPCC datasets
are used to update monthly data. The data used for
this study are historical series of monthly
precipitation from January 1901 to December 2016
that covers the entire Iberian Peninsula. Visually,
they are represented in the form of a grid, in an area
of 0.5ºx0.5º latitude-longitude (Figure 1). Therefore,
the total volume of information takes into account
the total months, years and cells that compose the
spatial extent. The amount of information of more
than 400,000 records with spatial connection
requires the use of a database management system.
Given the nature of the precipitation data grid,
which is not graphically adjusted to the actual
physical limits of the Iberian Peninsula, it is
necessary to adapt the rainfall information to the
limits of the field of study. For this purpose a vector
layer containing the physical boundaries of the
GIS and Geovisualization Technologies Applied to Rainfall Spatial Patterns over the Iberian Peninsula using the Global Climate Monitor
Web Viewer
different territorial units that make up the study area
(Spain and Portugal) was also used. The 1:
1,000,000 territorial statistical units (NUTS) of the
European territory for the year 2013 data have been
obtained from the European Statistical Office
(Eurostat, The
Coordinate Reference System through which this
information is distributed is ETRS89 (EPSG: 4258),
which is an inconvenience when combining this with
the rainfall data projected in WGS84 Spatial
Reference System used in the GCM. Therefore,
through a geoprocess of coordinate transformation
both systems were squared.
The assembly of the available open-source
technologies used in this study is shown in table 2.
The use of spatial data server, map server, web
application server and web viewers allow scientists
to undertake these types of macro-projects based on
the use of Big Data information.
Table 2: Open-source technologies used in this study.
Software / Application Use
PostgreSQL / PostGIS Spatial database management
system, open source, which
has been used in this work
PgAdmin Open source administration
and development platform
for PostgreSQL
QGIS Free and open source desktop
GIS used for export
database’s results
QGIS Cloud Free geoviewer web used for
results representation and
facilitating their distribution
Particularly noteworthy is the role of the spatial
data server PostGIS in handling spatial and
alphanumeric information and charts based on a
relational system PostgreSQL. The data are natively
encoded in said system providing a high
performance and allowing the use of any analytical
functions required; but the most importantly reason
for using PostgreSQL / PostGIS is its geographic
relational database that make possible to carry out
geoprocessing without having to leave the
processing core. This allows the regionalization of
data in a quasi-automatic way for any territorial
scales of interest based on SQL language.
Many tools in this field are more or less
equivalent such as SciDB database that manages
multidimensional cubes (especially suitable for
satellite images processing) or some scientific
libraries found in R or Python. Nevertheless these
technologies present a different approach. While it is
true that for raster spatial applications using time
series data is the ideal environment, it can be argued
that in terms of data structure the same effect can be
obtained with a more conventional relational
approach. Such is again the case of R and Python
programming language that cover the same scientific
needs with different technologies. In any case, a
geographic relational database can also replace these
technologies in many scenarios like the one
presented in this work. Another advantage is that
PostgreSQL / PostGIS can be directly coupled to R
( thus
obtaining a geostatistical database and enlarging the
possibilities of use and applications.
Through QGIS Cloud a web geoviewer was
developed as an extension of the QGIS. The
resulting maps of this study are represented in a
Open source systems fit the main aim of the Global
Climate Monitor project: rapid and friendly
geovisilization of global climatic dataset and
indicators to experts and non-experts users instead of
focusing in data analytics.
The methodology followed in this work is presented
in Figure 2. First, the data were downloaded from
the GCM in csv format and converted into a
database to be modelled. The proposed theoretical
model defines the conceptual and relational
organization which generates the physical database
itself. The conceptual model is simple and consists
of several tables: the meteorological stations table,
the monthly precipitation table values and the table
with the geographical limits of the Iberian Peninsula.
Then, a series of queries are performed in SQL
language on the monthly precipitation series of data
in order to get the seasonal and annual values as well
as the statistical measures calculated from them.
The added value and contribution of using GIS
and geospatial databases is that both allow massive
geospatial time and space analysis and
geovisualization. The aim is to get a series of
statistics that will help us characterize the spatial
distribution of the precipitation gridded series. The
analysis of variability, dispersion, maximum and
minimum and frequency histograms of the
precipitation series in the Iberian Peninsula
determined the need of using adequate statistical
measures to fulfil our goals.
This method of work by the management of a
database system allows the analysis of precipitation
GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management
Figure 2: The methodology flow chart.
at three different levels; a joint analysis of the
historical series, where all the monthly values
recorded in the database are collected; seasonal
analysis obtained by grouping months with a
theoretically similar behaviour that allows the
comparison between them; and finally intra-seasonal
analysis, which gives the possibility of studying the
statistically behaviour and variability of precipitation
within the months of each season.
In the Mediterranean area climate variables, and
particularly precipitation, present an extremely
variable and irregular behaviour, so the frequency
distributions tend to be asymmetrical. Typically,
non-robust statistics (mean, standard deviation and
variation coefficient) are commonly used in this type
of climatic studies. But these measures are
susceptible to extreme values that may detract their
statistical representativeness. For this reason we
decided to incorporate robust statistics to eliminate
this effect and to be able to assess the differences
between them (robust and non-robust) in both
absolute and relative statistics that are shown in
table 3.
It is important to note that, despite the relative
simplicity of these calculations many results can be
obtained due to the potential offered by the use of
spatial databases.
Table 4 shows the statistical measures calculated
at different time scales (annual, seasonal and
monthly) for each precipitation series. The result has
provided a total of 136 outcomes, which were
viewed using GIS, each with their corresponding
cartographies for the entire Iberian Peninsula.
Table 3: Statistical measures calculated for each series.
Not Robust
Mean ()
deviation (s)
Coefficient of
variation (CV)
Robust Median
range (IRQ)
coefficient of
variation (ICV)
( – Me)
(s – IRQ) -
Table 4: Statistical measures calculated at different time
of central
measures of
measures of
Annual 3 3 2
Seasonal 12 12 8
Monthly 36 36 24
Total 51 51 34
The statistics obtained for each of the cells are
incorporated into the open source Geographic
Information System QGIS. This is very useful when
carrying out multitude of analysis processes or
simply performing a cartographic representation of
the results. Finally, each map outcome is included in
a new web geoviewer (
GIS and Geovisualization Technologies Applied to Rainfall Spatial Patterns over the Iberian Peninsula using the Global Climate Monitor
Web Viewer
The management of large volumes of precipitation
information through spatial-temporal databases has
made possible to obtain products of relevant climatic
interest related to the spatial estimation of
precipitation in the context of water resources
management. The main results are presented first
focusing on statistical measures of central tendency
and measures of variability. The comparison and
quantification of non-robust and robust statistical
measures spatially represented in maps by means of
GIS allow the evaluation of the effect produced
when incorporating non-robust statistics rarely used.
Relative dispersion statistical measures are also
calculated to diminish the effect of the very different
amounts of rain recorded in the Iberian Peninsula.
Concerning measures of central tendency the
picture obtained by calculating the mean or the
median perfectly identified the three large
homogeneous climatic zones traditionally described
for the Iberian Peninsula. The first, called the
Atlantic or humid region, corresponds to the
Mesothermal climates, which extend over most of
the North coast from Galician to the Pyrenees; the
second, corresponding to semi-arid or sub-desert
region, occupies the southeast of the peninsula
around the province of Almeria; and the third is the
most extensive region with Mediterranean climate
that occupies the greater part of the Iberian
Peninsula. The spatial division basically matches the
800-mm isohyet separating the humid zone from the
Mediterranean ones and 300-mm that delimit the
southeast semi-arid area.
Comparing the robust (median) to the non-robust
(mean) measures it can be stated that there is a
general overestimation of precipitation. The map of
the difference between mean and median shows the
predominance of greenish tonalities which represent
mean precipitation values above medium precipitation
(Figure 3c). This is a consequence of the positive
symmetry of the annual precipitation distributions due
to the presence of very rainy years with respect to the
rest of the series. The areas where precipitation is
overestimated are mainly located in the south
normally characterized by low levels of precipitation.
In relation to the non-robust dispersion statistics
(standard deviation) and robust (interquartile range)
the most characteristic of both maps is the presence
of a marked NW-SE gradient (Figure 4). Inside the
Iberian Peninsula and great part of the south and
southeast dispersion values are less than 150 mm
linked to lower precipitation records. Therefore,
where the annual totals are higher, a greater
dispersion is observed in the precipitation values.
Standard deviation values are markedly higher than
the interquartile range except for the humid zones of
the north Atlantic coast. In the map of the
differences between standard deviation and
interquartile range it can be seen the latter
concentrating the greatest differences in the Atlantic
coast. These differences indicate the heterogeneity
of the precipitation values as a function of their
mean and median, which shows the high irregularity
in certain areas of the Iberian Peninsula. This is a
surprising result since these zones are usually
characterized by their pluviometric regularity.
Figure 3: Cartography of annual centrality statistical
(1901-2016); a) Mean precipitation, b) Median
precipitation, c) Difference between mean and median.
GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management
Figure 4: Cartography of annual absolute statistical
dispersion (1901-2016); a) Annual standard deviation, b)
Annual interquartile range, c) Difference between standard
deviation and interquartile range.
Finally, in order to remove the effect con
magnitude of precipitation totals, relative dispersion
statistics (Pearson Coefficient of Variation and
Coefficient of Interquartile Variation) were calculate
to compare different zones of the peninsula. In the
first map (Figure 5) it can be observed a
considerable decrease from north to south and even
in most of the Mediterranean coast.
The maps evidence considerable geographic
coherence and identify the areas with most rainfall
contrast. The country becomes distinctly divided
into the northern coast, where the stronger influence
of Atlantic disturbances produces more regular daily
rainfalls, and the rest of the territory. The
Mediterranean depressions produce highly
contrasting amounts (sometimes very large)
especially the Mediterranean side of the Peninsular
due to its scarce annual precipitation.
Though the Pearson Coefficient of Variation
(CV) has been already used for precipitation studies
in the Iberian Peninsula (Martín-Vide, 2011a), the
Coefficient of Interquartile Variation has not been
used previously (Figure 5b). It shows a less defined
pattern than the CV and with much higher values of
variation. The most notable is the low variability,
with respect to the rest of the territory registered in
the northern part as well as the presence of higher
levels in the Mediterranean coast and the Southwest
area of Atlantic influence decreasing as it penetrates
the interior of the peninsula.
Figure 5: Cartography of annual relative statistical
dispersion (1901-2016); a) Annual coefficient of variation,
b) Annual interquartile coefficient of variation.
These differences are not depreciable since in
many areas, such as the southwest of the Peninsula
corresponding to the Guadalquivir river valley, 80%
of the water resources are destined to the demand of
GIS and Geovisualization Technologies Applied to Rainfall Spatial Patterns over the Iberian Peninsula using the Global Climate Monitor
Web Viewer
irrigated agriculture. So far, the presence of many
reservoirs manages to satisfy and balance changes in
the availability of water, but the expected changes
due to climate change, with increasing temperatures,
evapotranspiration and extreme events threaten a
management system that has already exceeded the
natural limits of the resource. For this reason our
results are particularly relevant in these areas where
major imbalances and drought problems occur.
There should be a precise estimation of water
resources in order to be preparing for climate change
adaptation and mitigation measurements.
In addition, the simpler and more efficient way
to show all the results obtained in this work is to
make them accessible in a geovisor. This was made
by using QGIS Cloud, a web geovisor developed as
an extension of the QGIS that allows the publication
in a network of maps, data and geographic services.
This geovisor has all the potential of a cloud storage
and broadcast system that provides such a spatial
data infrastructure.
In short, it is a platform through which all
information and data capable of owning a
geographic component can be shared, according to
the standards of the Open Geospatial Consortium
(OGC), represented on the web through WMS
services and downloadable in WFS. Results of this
study can be viewed in
The complexity of the spatial rainfall pattern in the
Iberian Peninsula determined by many factors was
such as the relief, the layout, orientation or altitude
atmospheric circulation. All of them make even
more difficult a generalized characterization of the
Iberian precipitation spatial distribution.
Using the data from the Global Climate Monitor
it is possible to carry out this type of studies. This
climatic data geo-visualization web tool can greatly
contribute to provide an end-user tool for climatic
spatial patterns discovery. Compared to other geo-
viewers it hast objective advantages such as the easy
way to access and visualize climatic past and present
(near real-time) data, fast visualization response
time, variables and climatic indicators selection in a
unique client environment. The fast and easy way
for data downloading and exportation in some
different accessible formats facilitates the
development of climatic studies such as the one
presented here.
Using the precipitation data series provided by
the GCM, robust and non-robust statistics were
calculated for the period 1901-2016 at an annual and
seasonal scale. Robust statistical measures provided
different and complementary knowledge of
precipitation spatial distribution and patterns in the
Iberian Peninsula revealing a general significant
overestimation of precipitation. The consequences of
this for water resources estimation and allocation are
noteworthy for environmental management and
water planning and should be taken into account.
The two coefficients of variation used emphasized
the high irregularity behaviour more than it has been
traditionally considered and reveals different zones
of maximum variability.
The most novel contribution of this work is to
incorporate non-robust statistical measures and the
comparison with the most comely used ones. The
results show important variations in the estimation
of the amount of available water resource coming
from precipitation. The estimation of the differences
between statistics at monthly scales and by river
basins is to be achieved. All this results can be
useful in the knowledge spatial and temporal
distribution of precipitation and, therefore, in the
initial computations of the available water resources
of river basins for water management (commonly
estimated by few meteorological stations and not
updated periods of time). Nevertheless, the effect of
mountain ranges on the GCM data needs to be
evaluated when considering river basins.
In addition, web-based GIS and geovisualization
enable that the results obtained can be displayed in
maps and seen in a new web geovisor
This is largely novel since usually obtained results in
this type of studies are not shared by means of any
tool to give greater diffusion through the web.
Improving the interface and making it more user-
friendly based on the experience of users is a
constant goal of the Global Climate monitor project.
In future work we would also like to evaluate the
statistics used in this study at a global level and for
large areas relating the results with climatic
typologies. We will also like to compare the
evolution and changes of precipitation amounts
between different standard periods (climatic normal)
and other climatic indicators. Other climatic
variables will be also estimated and incorporated
into the Global Climate Monitor geoviewer. This is
an added value to this work concerning not only
about the generation of quality climate information
and knowledge, but also making it available to a
large audience.
GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management
Cabello Villarejo, V., Willaarts, B.A, Aguilar-Alba, M. &
Del Moral Ituarte, L. (2015). River basins as socio-
ecological systems: linking levels of societal and
ecosystem metabolism in a Mediterranean watershed.
Ecology and Society, 20(3):20.
Carslaw, D. C., & Ropkins, K. (2012). Openair — An R
package for air quality data analysis. Environmental
Modelling & Software, 27–28, pp. 52-61.
Casado, M.J., Pastor, M.A. & Doblas-Reyes, F.J. (2010).
Links between circulation types and precipitation over
Spain. Physics and Chemistry of the Earth, Parts
A/B/C, 35(9), pp.437–447.
Climate Research Unit (n.d.). British Atmospheric Data
Center. Retrieved from:
Cortesi, N., González-Hidalgo, J. C., Trigo, R. M., &
Ramos, A. M. (2014). Weather types and spatial
variability of precipitation in the Iberian Peninsula.
International Journal of Climatology, 34(8), 2661–
De Castro M, Martin-Vide J, Alonso S. (2005). El clima
de España: pasado, presente y escenarios de clima para
el siglo XXI. Impactos del cambio climático en
España. Ministerio de Medio Ambiente: Madrid.
Edwards, P.N., Mayernik, M.S., Batcheller, A.L., Bowker,
G.C., Borgman, C.L., 2011. Science friction: Data,
metadata, and collaboration. Soc. Stud. Sci. 41, 667–
690. doi:10.1177/0306312711413314
Folland, C.K., Karl, T.R., Christy, J.R., Clarke, R.A.,
Gruza, G.V., Jouzel, J.. Mann, M., Oerlemans, J.,
Salinger, M.J. & Wang, S.W. (2001). Observed
Climate Variability and Change. In: Climate Change
2001: The Scientific Basis. Contribution of Working
Group I to the Third Assessment Report of the
Intergovernmental Panel on Climate Change. (pp. 99 -
181). Cambridge: Cambridge University Press.
García-Barrón, L., Aguilar-Alba, M. & Sousa, A. (2011).
Evolution of annual rainfall irregularity in the
southwest of the Iberian Peninsula. Theoretical and
Applied Climatology, 103(1–2), pp.13–26.
García-Barrón, L., Morales, J. & Sousa, A. (2013).
Characterisation of the intra-annual rainfall and its
evolution (1837–2010) in the southwest of the Iberian
Peninsula. Theoretical and applied climatology,
114(3–4), pp.445–457
García-Barrón, L., Aguilar-Alba, M., Morales, J. &Sousa,
A. Forthcoming (2017). Intra-annual rainfall
variability in the Spanish hydrographic basins,
International Journal of Climatology.
Global Precipitation Climatology Centre (n. d.) Product
Access: Download. Retrieved from: http://www.
Hidalgo-Muñoz, J.M. et al. (2011). Trends of extreme
precipitation and associated synoptic patterns over the
southern Iberian Peninsula. Journal of Hydrology,
409(1), pp.497–511.
Intergovernmental Panel on Climate Change (2014).
Climate Change 2013 – The Physical Science Basis
Working Group I Contribution to the Fifth Assessment
Report of the Intergovernmental Panel on Climate
Change. Retrieved from:
Jones, P.D. & Moberg, A. (2003). Hemispheric and large-
scale surface air temperature variations: An extensive
revision and an update to 2001. Journal of Climate 16,
pp. 206-223.
Jones, W. R., Spence, M. J., Bowman, A. W., Evers, L., &
Molinari, D. A. (2014). A software tool for the
spatiotemporal analysis and reporting of groundwater
monitoring data. Environmental Modelling &
Software, 55, pp. 242-249.
Krysanova, V. et al. (2010). Cross-comparison of climate
change adaptation strategies across large river basins
in Europe, Africa and Asia. Water Resources
Management, 24(14), pp.4121–4160.
López-Bustins, J. A., Sánchez Lorenzo, A., Azorín
Molina, C., & Ordóñez López, A. (2008). Tendencias
de la precipitación invernal en la fachada oriental de la
Península Ibérica. Cambio Climático Regional Y Sus
Impactos, Asociación Española de Climatología, Serie
A, (6), 161–171.
Martín Vide, J. (2011a): ‘Estructura temporal fina y
patrones espaciales de la precipitación en la España
peninsular’. Memorias de la Real Academia de
Ciencias y Artes de Barcelona, 1030, LXV, 3, 119-
Martin-Vide J. (2011b). Patrones espaciales de
precipitación en España: Problemas conceptuales. In
Clima, ciudad y ecosistema, Fernández-García, F.,
Galán, E, Cañada, R. (eds). Asociación Española de
Climatología Serie B, nº 5; 11-32.
Mitchell, T. D. & Jones, P. D. (2005). An improved
method of constructing a database of monthly climate
observations and associated high-resolution grids.
International Journal of Climatology, 25, pp. 693–
Muñoz-Díaz, D. & Rodrigo, F.S. (2006). Seasonal rainfall
variations in Spain (1912–2000) and their links to
atmospheric circulation. Atmospheric Research, 81(1),
New, M., Hulme, M. & Jones, PD. (2000). Representing
twentieth century space–time climate variability. Part
2: development of 1901–96 monthly grids of terrestrial
surface climate. Journal of Climate, 13. pp. 2217–
Ríos-Cornejo, D. et al. (2015). Links between
teleconnection patterns and precipitation in Spain.
Atmospheric Research, 156, pp.14–28.
Vitolo, C., Elkhatib, Y., Reusser, D., Macleod, C. J. A., &
Buytaert, W. (2015). Web technologies for
environmental Big Data. Environmental Modelling &
Software, 63, pp. 185-198.
GIS and Geovisualization Technologies Applied to Rainfall Spatial Patterns over the Iberian Peninsula using the Global Climate Monitor
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