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) (http://www.globalclimatemonitor.org)

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 (https://qgiscloud.com/Juan_Antonio_Geo/expo) in order to facilitate

access to them.

1 INTRODUCTION

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

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 (https://qgiscloud.com/

Juan_Antonio_Geo/expo).

2 STUDY AREA AND DATA

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

Spatial

resolution

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

Spatial

Reference

System

WGS84 WGS84 WGS84

Format netCDF Grib netCDF

Variables

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, http://ec.europa.eu/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

(http://www.joeconway.com/doc/doc.html) 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

geoviewer

(https://qgiscloud.com/Juan_Antonio_Geo/expo).

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.

3 METHODOLOGY

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.

Centrality

statistical

Absolute

statistical

dispersion

Relative

statistical

dispersion

Not Robust

Mean ()

Standard

deviation (s)

Coefficient of

variation (CV)

Robust Median

(Me)

Interquartile

range (IRQ)

Interquartile

coefficient of

variation (ICV)

Difference

( – Me)

(s – IRQ) -

Table 4: Statistical measures calculated at different time

scales.

Time

scale

Measures

of central

tendency

Absolute

measures of

variation

Relative

measures of

variation

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 (https://qgiscloud.com/

Juan_Antonio_Geo/expo).

GIS and Geovisualization Technologies Applied to Rainfall Spatial Patterns over the Iberian Peninsula using the Global Climate Monitor

Web Viewer

4 RESULTS

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 https://qgiscloud.com/

Juan_Antonio_Geo/expo.

5 CONCLUSIONS

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

(https://qgiscloud.com/Juan_Antonio_Geo/expo).

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

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