Computers & Geosciences 48 (2012) 337–339

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Computers & Geosciences

0098-30

doi:10.1

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st20195

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Short note

Dbclim: A web-based, open-source relational databasefor rainfall event studies

G. Nigrelli a,n, A.L.L. Marino b

a Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, Strada delle Cacce 73-10135 Torino, Italyb Universit �a degli Studi di Torino, Dipartimento di Informatica, Corso Svizzera 185-10149 Torino, Italy

a r t i c l e i n f o

Article history:

Received 11 November 2011

Received in revised form

30 January 2012

Accepted 3 February 2012Available online 10 February 2012

Keywords:

Open source solutions

Database

Climate data

Rainfall events

1. Introduction

Rainfall is a climatic parameter of great importance andcomplexity. The pluviometric parameters ordinarily taken formeteo-hydrologic study are rainfall total and maxima (annual,seasonal or monthly), maximum rainfall over several consecutivedays (by definition usually 1, 2, 3, 4 and 5 days), rainfall duringbrief but intense rainstorms, and peak rainfall within temporalunits (minutes, hours, days). Values of absolute extremes, such asthe highest 5-day precipitation amount in a year, can often berelated to extreme events that affect human society and thenatural environment (Klein Tank and Zwiers, 2009).

Moreover, the data of some of these parameters may bederived from various different rain events and an event can lastmore than 5 consecutive days. In studies on geo-hydrologic riskmitigation, flood risk prevention and advance warning in civilprotection, for example, it is also important to evaluate not onlyhow much rain but how it falls. This can be done by considering arain event (RE) as a defined meteo-climatic variable. For thesepurposes it is useful to define a given RE as one or moreconsecutive rainy days with a daily rainfall depth Z1 mm. Thisapproach is essential to studying rainfall events within a broaderscope because the RE acts directly on landscape modeling.Depending on duration, total amount of rainfall, peak amount of

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esponding author. Tel.: þ39 0113977824; fax: þ39 0113977820.

ail addresses: guido.nigrelli@irpi.cnr.it (G. Nigrelli),

4@educ.di.unito.it (A.L.L. Marino).

rainfall, and the way and season in which it occurs, a RE maytrigger natural phenomena carrying high hazard and risk.

An innovative tool for RE study developed by the ResearchInstitute for Geo-hydrological Protection of the National ResearchCouncil (CNR-IRPI) is a specific web-based relational databasecalled Dbclim. With the Dbclim, RE data can be easily accessedand extracted from a long-term daily rainfall time series. The keyfeatures of Dbclim and its architecture are outlined below.

2. Database architecture and technology

Dbclim was designed to manage an archive of heterogeneoustypes of national climatic data. Its basic purposes are to collectand store weather data, as well as to provide access to climaticdata and products for use by researchers, climatologists, decisionmakers, and students. Dbclim was designed and developedfollowing World Meteorological Organization standards andrequirements for climate data management (WMO, 2007, 2011).The relational database’s modular architecture is extensiblethrough tables (currently 30 tables, for a total of 151 fields).Fig. 1 illustrates the schematic structure of Dbclim.

The Dbclim can manage 12 climatic parameters (daily data), eachof which is stored in a specific table (table name in italics): meantemperature (meantemp); minimum temperature (mintemp); maximatemperature (maxtemp); rainfall (rain); atmospheric pressure (atm-

pressure); relative humidity (relhum); wind velocity (windvel); winddirection (winddir); solar radiation (solarrad); snow depth (snow-

depth); water level (waterlevel); and water flow (waterflow).

Fig. 1. Schematic structure of the database: tables (squares) and links (lines). Relationship between two fields of two tables: 1-1, one-to-one; 1-N, one-to-many; N-M,

many-to-many. jt, junction table.

G. Nigrelli, A.L.L. Marino / Computers & Geosciences 48 (2012) 337–339338

There are six environment tables: statlocation (name, code);geography (elevation, slope, morphology and other); igmubic

(latitude, longitude); climreg (Alpine, Adriatic, Tyrrhenian climaticregion and others); landuse (urban area, agricultural area, forestarea, natural park and other); and vegetation (lawn, shrubs,wood). Added to these is the stations table which containsinformation about land-based weather stations.

Location tables contain the lists of official Italian administra-tive subdivisions (region and municipality), a detailed list of Poriver basins with all exorheic basins in Italy (riverbasin andsubbasin), and the data source (source).

Additional tables (attachments and users) are linked to thestations table in order to manage documents, maps, photos, loginparameters of each user, user time, type and data entry activities.Finally, two other tables (sensorstation and sensors) linked to thestations table report the type of sensors installed on each weatherstation. Environment, location and sensorstation are junction tablesthat associate these key data parameters with each climaticstation when the relationships between two fields of two tablesare many-to-many.

The relational database and web application were developedon a Microsoft Windows Server 2003 operating system throughopen source solutions (Apache 2.2.4, MySQL 5.0.45, and PHP5.2.4). A JavaScript jQuery framework that exploits Ajax technol-ogy was also integrated (Marino, 2007). Data persistence isguaranteed by a RAID 5 server configuration with three harddisks and by daily backups through an external network attachedstorage (NAS) unit. The web interface includes 169 pages thatusers can access through a simple and intuitive graphical userinterface. The database access has three different login levels foreach user assigned by the administrator: internal user (for theinstitutional activity of the CNR-IRPI); external expert user (forcollaborative research with other institutions similar to ours); andstudent user (for thesis and specific studies). The Italian geos-cientists can participate with their own data.

3. Data acquisition, web search query and exporting theresults

New daily data already in digital form and validated can bereadily ingested directly by the system (comma-separated values

file format [CSV]) through a simple download page. CSV is asimple file format that is widely supported by consumer, busi-ness, and scientific applications. The download page displays twooptions: create a CSV file with 12 columns (one column for eachmonth) or a single-column CSV file. For both options the date ofbeginning (ddmmyyyy), date of end, weather station and climaticparameter (selected through a combo-box) must be entered.Nondigital records are validated and digitized during an entryprocess through a special webform very similar to a spreadsheet.The key entry system is efficient and easy for use by data entryoperators.

Thirty-four predefined parametric queries were created toenable users to query the database and retrieve relevant informa-tion (Fig. 2). The parametric queries can be broadly grouped into:Temperature, Rainfall; Water level/flow; Metadata; and Stationsinside. The queries allow users to browse the entire database andfocus the search on a specific climatic parameter or geographic area.In the group of interrogations called ‘‘Rainfall’’ there are two specialqueries for extracting RE data: rainfall event series above a defaultthreshold and annual maximum rainfall event series. Through thesetwo interrogations the whole series or the maximum RE for a givenyear can be extracted. The results report lists the following sevenitems: order number; date of beginning event; date of end event;duration of the event (number of days); total rainfall of the event(mm); total rainfall of the highest daily rainfall (mm); and date ofthe highest daily rainfall. These two queries constitute a trulyinnovative feature developed inside a climatological databasesystem. The information can be printed out or exported in PDFand CSV formats by pressing a specific query button.

Dbclim is available at http://dbirpi.to.cnr.it/db_clim/index.php(Italian version, see the registration form ‘‘Registrazione utente’’at the home page). Currently, Dbclim contains the daily datarecorded by 191 weather stations from 1913 to 2011.

A better understanding of rainfall events can help to informstudies on geo-hydrologic risk mitigation, flood and landslide riskprevention. In a context of global and regional environmentalchange, there is a growing need for systematic analysis of rainfalloccurrence, including measures of timing, intensity and duration,which, in turn, can be effectively used in the detection andunderstanding of environmental change (Dunkerley, 2008).Dbclim can provide a valid tool for conducting systematic analysisof rainfall data.

Fig. 2. Predefined parametric queries and export of the data.

G. Nigrelli, A.L.L. Marino / Computers & Geosciences 48 (2012) 337–339 339

References

Dunkerley, D., 2008. Identifying individual rain events from pluviograph records: areview with analysis of data from an Australian dryland site. HydrologicalProcesses 22 (26), 5024–5036.

Klein Tank, A.M.G., Zwiers, F.W., 2009. Guidelines on Analysis of Extremes in aChanging Climate in Support of Informed Decisions for Adaptation. WorldMeterological Organization, World Climate Data and Monitoring Programme,WCDMP Series, Report no. 72, 52 pp.

Marino, A.L.L., 2007. Progettazione e realizzazione di un database relazionale perla gestione di dati climatici acquisiti da stazioni meteorologiche di superficie.

Unpublished B.Sc. Thesis. Universit�a degli Studi di Torino, Torino, 58 pp.WMO, 2007. Guidelines on Climate Data Management. World Meterological

Organization, Geneva, WCDMP-no. 60, WMO-TD no. 1376, 67 pp.WMO, 2011. Guide to Climatological Practices, third ed. World Meterological

Organization, Geneva, WMO-no. 100, 180 pp.