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Status of a Brazilian Automatic Hydro-meteorological territorial network


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This paper presents a report on the status of a Brazilian network of automatic hydro-meteorological stations. These stations were projected, acquired and installed by the " Centro de Monitoramento e Alerta de Desastres Naturais – CEMADEN " in order to develop and implement a system for monitoring natural disasters. CEMADEN provides early warnings for natural disasters affecting Brazil, analyses and issues alerts associated to floods and impacts of severe droughts. We present the challenges of planning, installing and maintaining such a network, besides collecting and processing a large amount of data generated on an area comprising diversified environments and distinct socioeconomically scenarios.
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Status of a Brazilian Automatic Hydro-meteorological
territorial network
Sergio Celaschi1, and Ademir L. Xavier Jr.2
Abstract This paper presents a report on the status of a
Brazilian network of automatic hydro-meteorological stations.
These stations were projected, acquired and installed by the
“Centro de M onitoramento e Alerta de Desastres Naturais –
CEMADEN in order to develop and implement a system for
monitoring natural disasters. CEMADEN provides early
warnings for natural disasters affecting Brazil, analyses and
issues alerts associated to floods and impacts of severe droughts.
We present the challenges of planning, installing and maintaining
such a network, besides collecting and processing a large amount
of data generated on an area comprising diversified
environments and distinct socioeconomically scenarios.
Keywords: natural disaster, wireless network, monitoring, data
Studies related to anthropogenic climate change presented
in the first Intergovernmental Panel on Climate Change report
(IPCC, 1990) have already highlighted the possible influence
of climate change on the frequency and severity of extreme
weather events, which are the main "triggers" for the
occurrence of natural disasters of hydro-meteorological origin,
such as floods, mudslides, landslides, collapses of yield crops,
and of water supply systems by droughts. According to the
Special Report on Managing the Risks of Extreme Events and
Disasters to Advance Climate Change Adaptation - SREX
(IPCC, 2012), even without taking into account climate
change, disaster risk will continue to increase in many
countries, including Brazil (Xavier and Celaschi 2016), since
more people and vulnerable assets will be exposed to extreme
weather, for example, in the outskirts of large cities.
The changes in global climate together with the incidence
of severe climatic anomalies (Horel and Wallace 1981,
Trenberth 1990, Mearns 2010) have triggered a worldwide
initiative toward acquiring and processing a large amount of
data with the aim of providing better forecast models and
reliable warning systems (Basher 2006, Hughes 2006, Koike
2009). It is understood that the challenge of providing such
warning services grows immensely with the population density
and country area. Moreover, the efficiency of Government
initiatives of this kind is far from being restricted to the
warning system set-up: the degree of preparedness of a given
area (Alfieri 2012, Thielen 2009, Bartholmes 2009) in face of
severe occurrences is an obligatory subject of public
administrative policies. Prior to accessing the reliability of the
overall system, other more technical requirements must be met
in order to have an operational sensor network (Basha 2007,
Basha 2008). Any minimally operational network must survive
the exposure to natural elements, must cope with unpredictable
failures and restrictions in power budgets. Two models of
alarm generation may be conceived: a localized one, which
somehow implements an automatic mechanism of warning
using processing resources at every collecting station or a
centralized one, which is feed by data provided by stations that
work simply as passive elements. The network described here
is of this later type.
In face of the impossibility of controlling climate change
and following worldwide initiatives (Castillo-Effer 2004,
Bartholmes 2009), Brazil has very recently invested in
planning, installing, maintaining and controlling a network of
different sensors for hydro-meteorological data acquisition,
with the first aim of increasing the amount of available data
throughout its territory by enhancing the surface coverage
density (New 2000) of weather sensors. Recent flood
occurrences in Brazilian Itajaí and Mundaú rivers in 2008 and
2011, respectively and landslides in Rio de Janeiro in 2011
have show how vulnerable the population is in face of severe
weather and unresponsive administration. On the other side of
the hydrological scale, a never registered drought in the south-
east part of the country in 2012-2014 are currently restricting
access to water to over 40 million people and threatening a
reduction in the country's gross product, since most of the
Brazilian power generation infrastructure is hydroelectric based
(Nobre 2009, Marengo 2014).
The purpose of the article is to present an overview of this
national project coordinated by “Casa Civil” an organ of the
Brazilian Executive federal government. The 2012 National
Plan for Risk Management and Disaster Response is overview,
and the central hole of CEMADEN is pointed out. Section II
presents the project architecture and its limitations along with
the challenges of deployment and operation. Section III shows
the details of the different stages (phases) of installation. The
main conclusions are presented in Section IV.
A. Overview
In August 2012, the Brazilian federal government launched
the National Plan for Risk Management and Disaster Response
which large investments in joint actions. The goal was to
ensure safety for those who live in areas vulnerable to natural
disaster events. Preventive actions were also intended to
preserve the environment and cover 821 municipalities which
account for 94% of deaths and 88% of displaced and homeless
throughout the country. The observation network includes the
acquisition of 9 meteorological radar; 3375 automatic rain
gauges, whose data are transmitted continuously (online) to the
Centre's data platform every ten minutes; 1375 semi-automatic
rain gauges for the Project "Rain Gauges in Communities". The
network also includes installation of 500 sensors for
monitoring rain and water in the soil, as well 95 agro-
meteorological stations for monitoring of the semi-arid
Brazilian Northeast region, especially for prediction of the
likelihood of agriculture crop failure, as a result of severe
drought in the Brazilian semiarid region. Therefore, the
environmental observational network of CEMADEN includes
the acquisition of roughly 6,000 equipments, and over 75% of
them have already been installed. In most cases, the
information is transmitted via cell phone through technology
3G / GPRS for storage and management of data, prior to their
incorporation in the monitoring platform CEMADEN. All data
and information is available CEMADEN freely to the scientific
community and society in general, multiplying the intrinsic
value of these observational networks in generating new
knowledge and its application.
At CEMADEN, a multidisciplinary team of professionals
from areas such as hydrology, meteorology, geology,
engineers, geography, specialists on natural disasters, computer
engineering and computational infrastructure of operating data
was then hired to work on research and real time alerts.
According to its legal framework, the responsibilities of
CEMADEN are: to produce early warnings of relevant natural
disasters for protective and civil defense actions across the
country, supporting the actions of CENAD Centro Nacional
de Gerenciamento de Riscos e Desastres; to produce and
release studies aiming the production of necessary information
for planning and promotion of actions against natural disasters;
to develop scientific, technological and innovation capacity, for
continuing improving the natural disasters early warning; to
develop and implement observation systems for monitoring
natural disasters; to develop and implement computational
models, to operate computational systems needed to the
elaboration of alerts; to promote capacitation, training and
support to activities of post-grad in correlated areas of action.
The National Plan of Risk Management and Response to
Natural Disasters established different types of data collection
units. In the following, we describe the current number of
installed stations: i) ~3600 automatic pluviometric stations
named DCP-Pluvio (DCP or data collection platform, Fig. 1a),
individually composed of: one rain gauge, one cabinet for the
data logger, GPS and other functional units, a GSM/GPRS
communication link, one (solar) power module; ii) 500
automatic stations called DCP-Acqua (Fig. 1b), composed of
one cabinet for the data logger, GPS and other functional units,
one rain gauge, sensors to monitoring soil moisture at two
distinct depths (10 and 20 cm), that are primarily to be installed
in the semi-arid region plus a GSM/GPRS communication link,
one (solar) power module; iii) 95 automatic stations called
DCP-Agro (Fig. 1c), composed of one cabinet for the data
logger, GPS and other functional units, assembling a variety of
sensors: one rain gauge, 4 soil temperature sensors, 4 soil
moisture sensors, a differential anemometer and radiation and
air humidity sensors plus GSM/GPRS communication and
(solar) power modules; iv) 1700 semi-automatic stations called
DCP-Semi, each one composed of a rain gauge, battery and a
user friendly interface with a data logger, to be installed in risk
areas and are operated by local community teams. Teams
which are specially trained, aiming to promote the engagement
and awareness of local inhabitants that live on areas of risk and
v) ~280 hydrologic stations called DCP-Hidro composed of a
radar water-level sensor, rain gauge, video camera for river
monitoring, communication and (solar) power modules.
Fig. 1. Pictures showing the most common DCP models installed. From
left to right, (a) DCP-Pluvio, (b) DCP-Acqua and (c) DCP-Agro.
Presently, this network is under continuous growth and
maintenance as shown in the DCP-pluvio map presented in
Fig. 2. Each DCP-Pluvio works as a stand-alone gathering
node, periodically sending, via GSM/GPRS (Halonen et al
2003) and FTP protocol, two types of reports: a specific string
of weather data (according to the DCP sensor set) and a status
report. DCP-semi stations only implement a local memory
buffer which his owner must download in order to send it to
CEMADEN data server. The data volume processed at the
center is about 50 Gigabytes of daily data upload and 100
Gigabytes of data flow with 15000 external daily access.
CEMADEN monitoring service operates on 24/7 regime with
green, yellow, orange and red operational mode alarms, with at
least four experts on duty: a meteorologist, a hydrologist, a
geologist and an expert in natural disasters.
Fig. 2. Positioned DCPs “Pluvio” deployed by CEMADEN as in May
The National Plan defined several priority areas in the
country based on an initial risk analysis for the choice of each
site, depending on criteria such as: presence of radio base
stations less than 5 km away from intended DCP site,
deficiency of local hydro meteorological data, existence of risk
areas and population density. As shown in Figure 3(a), 51% of
the Brazilian population (~ 200 million inhabitants) is presently
attended by the network (that is, live in an area monitored by
one or several DCP). From this total 45% is regarded as
priority and less than 3% are still living on unattended sites. In
terms of city number, Figure 3(b) shows that 15% of cities are
located in risk areas and therefore are monitored. The 3%
remainder in Figure 3(a) and Figure 3(b) is still uncovered and
are natural installation targets for the next years. Finally, the
National Plan intends to monitor all areas, even if under the
non-priority status.
Fig. 3. (a) Percent of the total population (200 million inhabitats) assisted
by the network installation plan until 2014 according to monitoring and
priority status. (b) Percent of the total city number (5563) assisted by the
network until 2014. 15% or the total city number is monitored.
On the micro level, several are the challenges of installing
and maintaining this wireless network with a variety of sensor
types and configurations across the 8.5 million kilometers
squared of the Brazilian territory. The installation process has a
series of procedural steps not only to define ideal sitting but
also to warrant the proper equipment ownership at the
installation site. Since the site is rarely a public domain, prior
to installation, it must be inspected by an UNESCO advisory
team (Project 914BRZ2018/MCTI) and checked against a set
of minimum requirements. Ideal installation conditions involve
the choice of a minimally flat terrain and unobstructed areas
(stations located at least 10 m away from trees, buildings etc
and 30 m away from sealed roads or highways) due to solar
powering. Depending on the terrain type, options are not
readily available. For obvious reasons, the site should not be
located inside cultivated areas and, most important of all, from
the property headquarters the station must be clearly seen. If
the site is technically approved, the process goes to the time
consuming phase of acquiring formal documents and contract
signatures by local authorities and land owners.
A detailed block diagram of all the phases necessary for an
every DCP type installation is shown in Fig. 4. Each phase may
be further divided in the following steps:
phase 0 DCP site definition, database consulting
for evaluation of site potential risk, installation
mission planning, DCP configuration, logistics,
establishment of partner agreements;
phase 1, setup of installation kits, advisory team
formation, civil defense support request, field
work, preparation of inspection report, delivery of
installation authorization, database feeding and
phase 2 (if the site is approved), setup of
installation kit for local site, partner contact
support (government agencies, local authorities
etc), installation WBS (work breakdown structure)
and DCP data exchange test; and, if the
installation does not succeed,
phase 3, comprising system diagnostics, attempt of
implementing remote solution, field work (system
solution and optimization), eventual DCP
phase 4, quality test, network operation and DCP
Overall we report excellent public acceptance of both
advisory and installations teams. Locals are much more
concerned nowadays with the issues of climate change and the
impact on their lives of severe weather. In certain areas,
installation teams are even courteously received, since the
stations are viewed as a confirmation of administrative concern
for their lives and be protected from impact of extreme events.
Fig. 4. Block diagram representing the installation phases.
A block diagram of the DCP internal structure is shown
schematically in Fig. 5 bringing the common and main
elements for most DCPs and is specific for two types: a DCP-
pluvio and a DCP-acqua, the last one with an additional soil
humidity sensor shown with dashed lines in that figure.
External communication is provided by a General Packet Radio
Services (GPRS) modem (RS232/RS445 interfaces, Global
System for Mobile communication - GSM 900 MHz and GSM
1800 MHz bands, max. downlink rate ~90 kbps, max. uplink
rate ~42 kbps) and an external antenna of two types, depending
on the DCP location. In urban areas, a single monopole < 2 dBi
antenna gain is sufficient. Rural zones require higher gains and
the same GPRS modem is connected to a >10 dBi Log-periodic
antenna. The DCP data logger is the responsible for the
analog-digital conversion for all sensor units which include a
tipping bucket rain gauge (Habib et al. 2001, Wang et al. 2008)
(200 ± 0.5 mm bucket diameter, 500 mm/h capacity and ± 2%
or ±3 % accuracy in the 0-250 mm/h and 250 500 mm/h
interval, respectively) and internal humidity, temperature and
control box lock sensors. Such internal data measurements are
periodically registered and sent for maintenance reasons. The
power module has a battery bank (12V/36 Ah), a solar panel
(maximum power 20W/17.4V @ 25oC) and a charge control
Fig. 5. Schematic representation of DCP-acqua (added soil humidity
sensor) and DCP-pluvio.
The purpose of this section aims to present how the
GPRS/GSM network is important in the exchange of messages
between the DCPs. Today Brazil is one of the largest markets
in the world for mobile communication (GSMA London Office
2016). Recent data estimate that over 90% of the Brazilian
population is covered by mobile connections. VIVO is the
largest mobile operator in Brazil that is owned by Telefonica
and has 29 % of the Brazilian market share. The second largest
mobile operator in Brazil, TIM is owned by Telecom Italia
with 27 % of the Brazilian market share. CLARO is the third
largest mobile operator in Brazil that is owned by America
Mobil and has another 25 % of the Brazilian market share. The
fourth largest mobile operator in Brazil OI is currently owned
by CorpCo, a joint venture with Portugal Telecom. The
CEMADEN monitoring and alert system is served by those
four major GSM/GPRS/3G service providers.
A partial view of one layer of the CEMADEN national
monitoring network is shown in Fig. 6. The functional roles of
this network are accessible on time at At every single DCP
location you find the rain volumes for the last 4, 24 hours, and
the accumulated precipitation for the last seven days. Data
exchange at the CEMADEN DCP-Pluvio monitoring system
network (Fig. 6) is provided by the standard Global System for
Mobile Communications GSM, which has become the
default global standard for mobile communications world wise.
Data communications employs packet data transport via GPRS
(General Packet Radio Services). GPRS is a packet oriented
mobile data service on the 2G and 3G cellular communication
system's global system for GSM. A major advantage of GPRS
is its simplified access to the packet data networks like the
internet. The packet radio principle is employed by GPRS to
transport user data packets in a M2M structured way between
GSM DCP stations and external packet data networks. These
packets can be directly routed to the packet switched networks
from the automatic hydro meteorological stations. GPRS
throughput and latency are variables that depend on the number
of other users’ simultaneity sharing the service. The
GSM/GPRS transponders installed in the DCPs provides data
rates up to the third (3G) generation of mobile telephony. The
M2M communication interface for periodic data transmission
is heavily dependent on the four major cell phone carriers
mentioned above. Although the feasibility of such
communication system has been demonstrated, there are
clearly limits for both quality of service delivered (QoS;
national coverage area of the GSM/GPRS network, service call
time) and sensitivity to climate change (service loss during
heavy rainfall).
Fig. 6. Positioned DCPs “Pluvio” deployed by CEMADEN as in
11/05/2016. Different colors at the caption indicate the rain volumes in
The environmental observation network monitored by
CEMADEN is essentially to provide in situ information
relevant for monitoring activities and the elaboration of alerts
for those municipalities with risk areas of natural disasters.
Also, it is useful for subsides generating scientific knowledge,
which can help to understand environmental phenomena
involved and triggering of natural disaster related to geo-
hydro-meteorological conditions in order to continually seek
significant improvements in the prediction of these
phenomena as well as improving the advance and precision of
natural disaster alerts issued by the institution. In addition, as a
Centre of science technology and innovation, is committed to
generate scientific knowledge and technological advances in
the area of natural disasters, culminating in applications
relevant to society.
We are grateful to Dr. Carlos A. Nobre, Dra Regina C.S.
Alvalá, Dr. Marcelo E. Seluchi, from CEMADEN, Marcos A.
Rodrigues Sidney Cunha and Germano Beraldo from CTI for
important discussions. This work is supported by the MCTI
the Brazilian Ministry of Science, Technology and Innovation.
Special thanks to Silvestre R. de Aguiar Jr, from Coordenação
Geral de Meteorologia, Climatologia e Hidrologia -
SEPED/MCTI - Secretaria de Políticas e Programas de
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Autor 1 CTI Renato Archer, CEP 13069-901, Campinas, SP, Brazil, and Autor
FacTI - Fundação de Apoio à Capacitação em TI, 13092-599, Campinas, SP,
Brazil, E-mails:, This work
was partially supported by Centro de Monitoramento e Alerta de Desastres Naturais -
... These stations are composed of one cabinet for the data logger, GPS and other functional units, one rain gauge, sensors to monitoring soil moisture at two distinct depths (10 and 20 cm), plus a GSM/GPRS communication link, one solar power module. Besides these, other 95 automatic stations called DCP-Agro, composed of one cabinet for the data logger, GPS and other functional units [16] have been installed. ...
... Map of Brazil showing the location of some of the CEMADEN in situ stations[16]. ...
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This paper describes the cubic spline-based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss-Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4-7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h-1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity-rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.
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In this study we investigated sampling-related errors of tipping-bucket (TB) rain gauge measure-ments, focusing on the gauge's ability to represent the small-scale rainfall temporal variability. We employed a simple TB simulator that used ultra-high-resolution measurements from an experimental optical rain gauge. The simulated observations were used to provide TB rain rate estimates on time scales as low as one minute. The simulation results showed that the TB estimates suffer from significant errors if based on time scales less than 10 to 15 minutes. We provide the approximate formulas used to characterize the TB sampling errors at several time scales. Our results show the importance of using fine resolution of both the sampling time interval and the bucket size so that the TB rain rate estimates have minimum levels of uncertainty.
Conference Paper
This report provides an overview of the Brazilian Centre for Natural Disaster Monitoring and Alerts (CEMADEN -
This paper focuses on the tracking of mesoscale convective systems (MCS) from geostationary satellite infrared data in the tropical regions. In the past, several automatic tracking algorithms have been elaborated to tackle this problem. However, these techniques suffer from limitations in describing convection at the “true” scale and in depicting coherent MCS life cycles (split and merge artifacts). To overcome these issues, a new algorithm called Tracking Of Organized Convection Algorithm through a 3-D segmentatioN has been developed and is presented in this paper. This method operates in a time sequence of infrared images to identify and track MCS and is based on an iterative process of 3-D segmentation of the volume of infrared images. The objective of the new tracking algorithm is to associate the convective core of an MCS to its anvil cloud in the spatiotemporal domain. The technique is applied on various case studies over West Africa, Bay of Bengal, and South America. The efficiency of the new algorithm is established from an analysis of the case studies and via a statistical analysis showing that the cold cloud shield defined by a 235-K threshold in the spatiotemporal domain is decomposed into realistic MCSs. In comparison with an overlap-based tracking algorithm, the analysis reveals that MCSs are detected earlier in life cycle and later in their dissipation stages. Moreover, MCSs identified are not anymore affected by split and merge events along their life cycles, allowing a better characterization of their morphological parameters along their life cycles.
The increasing demand by the scientific community, policy makers, and the public for realistic projections of possible regional impacts of future climate changes has rendered the issue of regional climate simulation critically important. The problem of projecting regional climate changes can be identified as that of representing effects of atmospheric forcings on two different spatial scales: large-scale forcings, i.e., forcings which modify the general circulation and determine the sequence of weather events which characterize the climate regime of a given region (for example, greenhouse gas abundance), and mesoscale forcings, i.e., forcings which modify the local circulations, thereby regulating the regional distribution of climatic variables (for example, complex mountainous systems). General circulation models (GCMs) are the main tools available today for climate simulation. However, they are run and will likely be run for the next several years at resolutions which are too coarse to adequately describe mesoscale forcings and yield accurate regional climate detail. This paper presents a review of these approaches. They can be divided in three broad categories: (1) Purely empirical approaches, in which the forcings are not explicitly accounted for, but regional climate scenarios are constructed by using instrumental data records or paleoclimatic analogues; (2) semiempirical approaches, in which GCMs are used to describe the atmospheric response to large-scale forcings of relevance to climate changes, and empirical techniques account for the effect of mesoscale forcings; and (3) modeling approaches, in which mesoscale forcings are described by increasing the model resolution only over areas of interest. Since they are computationally inexpensive, empirical and semiempirical techniques have been so far more widely used. Their application to regional climate change projection is, however, limited by their own empiricism and by the availability of data sets of adequate quality. More recently, a nested GCM-limited area model methodology for regional climate simulation has been developed, with encouraging preliminary results. As it is physically, rather than empirically, based, the nested modeling framework has a wide range of applications.
Preparedness towards natural hazards is a key factor in the reduction of their impact on the society. Recent international initiatives are fostering the development of a culture of risk prevention and the promotion of early warning systems. Numerical weather predictions have become the basis of several flood-related warning systems, enabling the detection of hazardous events with sufficient lead-time to prepare effective emergency and response plans. The objective of this paper is to review current European operational warning systems for water-related hazards induced by severe weather conditions. In details, it includes systems for detecting surface water flooding, flash floods, debris flows, mud flows, rainfall-induced landslides, river floods and coastal floods. Technical features and capabilities of different systems are described, together with some noteworthy examples. The main strengths of each system type are highlighted and suggestions are provided for developing and further improving their overall skills in hazard detection and the mutual coordination. © 2012 Elsevier Ltd.