Linkages between FAO agroclimatic data resources and the development of GIS models for control ...

14 Pages·2001·0.79 MB·English

Acta Tropica 79 (2001) 21–34
www.parasitology-online.com
Review
Linkages between FAO agroclimatic data resources
and the development of GIS models for control
of vector-borne diseases
Michele Bernardi *
En(cid:1)ironment and Natural Resources Ser(cid:1)ice, Food and Agriculture Organization of the United Nations,
Viale delle Terme di Caracalla,00100Rome, Italy
Abstract
TheFoodandAgricultureOrganization(FAO)oftheUnitedNationsisthelargestspecializedUNAgencydealing
with agriculture, forestry and fishery, particularly in the developing countries. One of its technical services, placed
under the Sustainable Development Department, has the responsibility to provide information on environment and
naturalresourcesasrelatedtofoodandagriculture.Itincludes,amongothers,expertiseinremotesensing,geographic
information systems and agrometeorology, production of global environmental digital datasets, meteorological and
remote sensing data collection and analysis at near real-time, development of methodologies, models and tools for
data standardization, collection, spatialization, analysis and dissemination, networking and information sharing,
development of integrated information management systems. Some experience has also been gained in the use of
climatic digital datasets for spatial modeling of crop pests and diseases. The description of mapping the distribution
oftheWesternCornrootworm(Diabrotica(cid:1)irgifera)inEuropeasafunctionofenvironmentalconditionsispresented
as well as the global assessment of environmental potential constraints based on processing of digital datasets. A
simple spatial interpolation routine is briefly explained. © 2001 Elsevier Science B.V. All rights reserved.
Keywords:FAO; GIS models; Vector-borne diseases
1. Introduction teorology Group. This situation is due to the
increasingneedforspatialgeo-referencedenviron-
During the past two years, the production of mental information and it has evolved together
digital maps based on climatic datasets has be- with the development of spatial interpolation
come an important activity of the FAO Agrome- techniques as well as the tools for data processing
and statistical analysis. Geographic Information
System (GIS) technology, combined with the use
*Tel: [phone]; fax: [phone].
E-mail address:[email protected] (M. Bernardi). of remote sensed imagery and agrometeorological
0001-706X/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.
PII: S0001-706X(01)00100-0
22 M. Bernardi/Acta Tropica79(2001)21–34
data and tools, has many uses in the area of environmental conventions and actions for small
spatial modeling of crop pests and diseases. The island states. SD also promotes sustainable rural
abilitytomanagelargeamountsofgeographically energy strategies and technologies, and helps de-
referenced data makes GIS technology an integral veloping countries with the use of remote sensing,
part of crop monitoring activities, including field agrometeorology, and GISs to manage natural
work planning, data organization, spatial model- resources and monitor the environment, including
ing activities, crop area estimates and yield crop conditions.
predictions. The Environment and Natural Resources Ser-
AGIScanbedefinedasanorganizedcollection vice(SDRN)isplacedwithintheSDDepartment.
of computer hardware, software, geographic data, SDRN is the integrated response to FAO’s envi-
models, and personnel designed to capture, store, ronmentalandnaturalresourcesprogramrequire-
update,manipulate,analyze,anddisplayallforms ments. SDRN deals with sustainable development
of geographically referenced information and natural resources and environment manage-
efficiently. ment issues in order to: (i) develop spatial infor-
GIS technology integrates spatial and other mation infrastructures that enable information
related information (attributes) within a single access, sharing, and lower costs; (ii) promote inte-
computer system and allows access to attribute grated data and information use for food security
information by geographic location. A GIS sys- and sustainable development; (iii) develop
tem may be used to answer such questions as: methodologies, guidelines, and policies for re-
(cid:1) What is at…? (condition) source management and environmental monitor-
(cid:1) Where is it? (location) ing; and (iv) coordinate the implementation of
(cid:1) What has changed since…? (trends) environmental conventions. SDRN has a multi-
(cid:1) What spatial patterns exist? (patterns) disciplinary team of experts in remote sensing,
(cid:1) What if…? (modeling/scenario) GIS, agrometeorology, environment, energy, ecol-
GIS integrates five key components: hardware, ogy, and economics.
software, data, people, and methods. GIS soft-
ware provides the functions and tools needed to 2.1. The Real-time En(cid:1)ironmental Monitoring
store, analyze, and display geographic informa- Information System
tion. The most important software components
are: Inthefieldofenvironmentalmonitoringremote
(cid:1) tools for the input and manipulation of geo- sensing imagery is managed by the Africa Real
graphic information; time Environmental Monitoring Information Sys-
(cid:1) a database management system; tem (ARTEMIS). Since August 1988, ARTEMIS
(cid:1) tools that support geographic query, analysis, has been operationally receiving, processing,
and visualization; archiving, and disseminating low-resolution re-
(cid:1) a graphical user interface for easy access to mote sensing imagery in support of FAO’s pro-
tools. grams on early warning for food security, migrant
pest, and disease control.
The ARTEMIS was implemented by FAO in
2. The Environment and Natural Resources close cooperation with the NASA Goddard Space
Service Flight Center, USA, the National Aerospace Lab-
oratory of the Netherlands, and the University of
FAO’s Sustainable Development Department Reading, UK, with funding from the Government
(SD) deals with environment and sustainability of the Netherlands.
issues. It advises governments on integrated pol- ARTEMIS is now fully operational and sup-
icy, planning, and management of natural re- ported by the FAO Regular Program. ARTEMIS
sources. It coordinates the FAO implementation supports the operational monitoring of seasonal
of the 1992 Earth Summit’s Agenda 21, global growing conditions and vegetation development
M. Bernardi/Acta Tropica79(2001)21–34 23
over Africa, based on hourly METEOSAT and earth observation data from the MODIS instru-
daily (NOAA) and advanced very high resolution ment on the TERRA satellites. Similar discus-
radiometer AVHRR data, for use in early warn- sions are ongoing with EUMETSAT and ESA
ing for food security and desert locust control. concerning the future METEOSAT Second Gen-
This includes the routine distribution of eration and ENVISAT satellite missions,
ARTEMIS images, containing information about respectively.
rainfall and vegetation activity, by electronic The ARTEMIS archive contains products
means to users at FAO headquarters and at re- derived from four different series of satellites by a
gional and national levels. FAO continues to sup- number of processing centers, which are available
port the establishment and/or improvement of at daily, 10-day, and monthly intervals. They can
local reception and/or processing systems using be seen at http://metart.fao.org where some
low-resolution environmental satellites, including agrometeorological products and tools are also
the development of improved interpretation tech- available.
niques and user-friendly analysis software.
ARTEMIS system has a direct reception capa- 2.2. Geographic Information Systems
bility for digital transmissions from the ME-
TEOSAT satellite. In addition, images recorded Most sustainable development decisions are
by the GOES and GMS satellites and relayed multidisciplinary or cross-sectoral. GIS technol-
through METEOSAT are received every 3 h. The ogy can help establish cross-sectoral communica-
METEOSATsystemisoperatedbyEUMETSAT. tion, not only by providing very powerful tools
The GOES system is operated by the US for the storage and analysis of multisectoral spa-
(NOAA). The GMS system is operated by the tialandstatisticaldata,butalsobyintegratingthe
Japanese Space Agency (NASDA) and the Japan databases of different sectors in the same format,
Meteorological Agency. structure, and map projection. GIS is used to
ARTEMIS uses the METEOSAT-based integrate, process, analyze, and display digital
product, such as the Cold Cloud Duration spatial and nonspatial data. Layers of thematic
(CCD), to estimate ground rainfall and the information can be integrated to provide new
NOAA/AVHRR-based assessments of vegetation insights into sustainable development problems.
cover, such as the Normalized Difference Vegeta- GIS refers to computerized information storage,
tion Index (NDVI), for operational monitoring of processing, and retrieval systems specifically de-
crop conditions to supply to the FAO Global signed to cope with spatial data and their corre-
Information and Early Warning System sponding attribute information.
(GIEWS). Assessment of crop growing conditions The core data holdings of the FAO GIS Unit
and related food production outlook is based on are described in Appendix A. The FAO GIS Unit
agrometeorological observations combined with also intends to update/enhance or develop the
remote sensing and other relevant socio-economic following global GIS database with regard to:
information. (cid:1) sub-national boundaries
In order to expand the capabilities of the (cid:1) geo-referenced socio-economic datasets
ARTEMIS system in support of food security, (cid:1) climate
crop forecasting, locust control, animal health, (cid:1) soils and soil degradation
water resources management, and forestry appli- (cid:1) soil constraints and problem soils
cations, FAO, in cooperation with the European (cid:1) agroecological zones
Commission through its Space Applications Insti- (cid:1) potential food self-sufficiency at low and high
tute of the Joint Research Centre, is currently input levels
implementing a routine flow of global 1-km reso- (cid:1) land-cover
lution vegetation data from the SPOT-4 satellite. (cid:1) ecofloristic zones
FAO is presently negotiating a formal agreement (cid:1) physiography (Africa)
with NASA for the development of the use of (cid:1) topography (DEM)
24 M. Bernardi/Acta Tropica79(2001)21–34
(cid:1) hydrography files and analyses, as well as some printed tables
(cid:1) water resources (Africa) by continents.
(cid:1) watershed basins Other activities of the Agrometeorology Group
(cid:1) irrigated lands include: (i) dissemination of a database of African
(cid:1) infrastructure sub-national crop statistics; (ii) maintenance of a
(cid:1) protected areas reference unit on climate, and climate change and
(cid:1) population and population density (rural/ur- agriculture; (iii) development and standardization
ban projections for 2000, 2005, 2010, 2015). of tools and methods of agroclimatic databases
and application software (agrometeorological
2.3. Agrometeorology data storage and processing, spatial interpolation
of climatic variables); (iv) crop monitoring and
The Agrometeorology Group of FAO provides yield forecasting: based on rainfall reports from
information on weather factors which are relevant FAOcountryrepresentatives,nearreal-timemete-
to agriculture. In the mid-1970s, most of the orological data, satellite imagery, and crop-spe-
activities of the Agrometeorology Group were cific water balance model (the outputs are
geared towards agroclimate/agroecological studies provided to the FAO global information and
which were carried out under the FAO Agro-Eco- early warning system(GIEWS)); (v) climate-re-
logical Zones (AEZ) Project and which produced lated risk in agriculture: analysis of climate risk
a large dataset of average monthly weather condi- and vulnerability applied to agricultural produc-
tions prevailing over the developing world. After tion and attempts to quantify, at a national and
thecompletionoftheAEZproject,theAgromete- global scale, the losses actually suffered by agri-
orology Group continued assembling historical, culture due to climate variability; and (vi) disaster
long-term rainfall data to serve the requirements impact assessment: evaluation of methodologies
of climate variability studies and climate risk as- for the rapid evaluation of geophysical disaster
sessment. At the same time, the Agrometeorology impact as related to the agricultural sector and
Group started collecting near real-time precipita- development of a detailed disaster impact data-
tion data from various sources for several hun- base as a tool for impact simulation.
dred stations around the world to be used for the
agrometeorological crop monitoring and yield
forecasting. 3. The Western Corn rootworm as an example
Agrometeorological data coverage includes
weather parameters of relevance to agriculture The analysis of distribution of the Western
(temperatures,precipitation,humidity,windspeed, Corn rootworm (Diabrotica (cid:1)irgifera) as a func-
sunshine fraction, radiation, and evapotranspira- tion of environmental conditions was carried out
tion potential). The data cover about 25,000 in 1997 jontly by the Agrometeorology Group of
weatherstationsworldwidebutthefocusisonthe SDRN and the Land and Plant Nutrition Man-
developing countries in Africa, South and Central agement Service. The assessment was based on
America, and Southeast Asia, and the Mediter- monthly values of air temperature as published in
ranean countries. The data include monthly aver- the International Institute for Applied Systems
ages and normals (30-year averages) as well as Analysis (IIASA) database as prepared by Lee-
time series. The listed averages cover the 1961– mans and Cramer (1991). The spatial resolution
1990 period. Data are from a number of sources, of the database is 0.5° of latitude and longitude.
mainly yearbooks and monographs published by
national climatological services, copies of elec- 3.1. Biology
tronic data obtained by FAO from national or
international data and research centers, as well as The Western Corn rootworm D. (cid:1)irgifera
public sources available on Internet. Agrometeo- (Coleoptera: Chrysomelidae) was introduced in
rologicaldataareavailableasdigitalmaps,digital the 1990s into Serbia (Yugoslavia). This is an
M. Bernardi/Acta Tropica79(2001)21–34 25
important pest of maize occurring in North the potential distribution of D. (cid:1)irgifera in Eu-
America, whose soil-inhabiting larvae can seri- rope. The analysis was based only on the values
ously damage the roots of maize (Zea mays) and of air temperatures. As long-term values (nor-
lead to yield losses. D. (cid:1)irgifera was first detected mals) of air temperature were used, it was as-
in July 1992 in the locality of Surcin near the sumed that the surface soil temperature will
Belgrade International Airport. The origin of this follow the air temperature with a lag of a couple
introduction remains unknown. Once introduced of days without large differences, particularly in
into Europe, D. (cid:1)irgifera started to spread at a spring–early summer when there is no snow
rather rapid pace in the Danube basin. Surpris- cover. Temperatures will be different during the
ingly, it was found in 1998 in Italy, near the snow melt period only.
airport of Venice. In 1999, it continued to spread A computer program was prepared to compute
in Central Europe, mainly northwards and south- the parameters assumed to be relevant for the
wards, but it was not found in new countries. development ofDiabrotica. Outputs from the pro-
Eggs are laid in the top 15 cm of soil, at the gram were mapped in two raster formats: GIF
base of the maize plants; they are the overwinter- and Image Display and Analysis (IDA). A raster
ing stage. They hatch over a prolonged period. format breaks an image into a grid of equally
The larvae develop in and on the roots, the young sized pieces, called ‘pixels’, and records color in-
larvae feeding on fine rootlets and the older ones formation for each pixel. GIF is one of the non-
invading the root core. Pupation takes place in geo-referenced raster formats while, IDA is a
the soil. The emerging adults move to the maize geo-referencedformatused,particularly,forsatel-
plant, feeding on the leaves and silks. Eggs nor- lite imagery and agrometeorological applications.
mally require a cold-induced diapause period be- IDA format images can be displayed and pro-
fore they will hatch, although a proportion of the cessed by using WINDISP software described in
population may hatch during a warm, prolonged Section 5. WINDISP is a public domain, easy to
autumn. use software package for the display and analysis
Under laboratory conditions 4–5°C was found of satellite images, maps, and associated data-
to be more suitable for diapause than 0°C or bases,withanemphasisonearlywarningforfood
lower. Hatching is reduced 50% by exposure to security.
−10°C for 1 week, and reduced to zero by 4 The parameters which are assumed to be rele-
weeks at that temperature or 1 week at −15°C. vant for mapping the distribution of D. (cid:1)irgifera
The threshold temperature for development is are as follows (Fig. 1):
12.8°C, the thermal requirements for hatching (cid:1) Tem — emergence temperature;
lying in the range 300–400 degree-days over the (cid:1) Tlow — low-temperature threshold;
threshold according to different reports. Larval (cid:1) Tth — threshold temperature used for the
development of D. (cid:1)irgifera took 71, 38, and 27 calculation of the sum of degree-days;
days at 15, 22, and 29°C, respectively. The pupal (cid:1) SDD — sum of temperature to maturity;
stage is short-lived. The adults are most active at (cid:1) Dt — number of days after maturity.
dawn and dusk. Evening temperatures of about The relevant output parameters computed by
18°C are optimal for oviposition. Both species the program are as follows:
mainly attack the maize (Z. mays), the larvae (cid:1) HatchN:‘Minimum hatch rate’, computed fol-
feeding on the roots and the adults feeding on the lowing the Woodson and Gustin (1993) equa-
leaves and silks. Larval feeding on the roots tion giving the hatch rate as a function of
causes root pruning. temperature and duration of exposure to the
temperature. Given a temperature Tlow, the
3.2. Methodology for mapping program computes when the temperature is
reached (day Dlt1) and the period during
Based on the literature provided, the criteria which the temperature stays below Tlow. This
described below were eventually adopted to map provides the variables needed for the Wood-
26 M. Bernardi/Acta Tropica79(2001)21–34
son–Gustin equation. The 12 hatch rates have values was driven by the following conditions:
been computed which correspond to Tlow val- (cid:1) condition 1 — areas where HatchN exceeds 0;
ues from 0 to −11°C, considered the 81% (cid:1) condition2— the cycle can be completed, and
hatch rate regardless of time of exposure and actually reaches the required SDD;
the ‘killing temperature’, respectively. (cid:1) condition 3 — the temperature of Dm+75 is
(cid:1) De:Day of emergence, corresponding to tem- less than 22°;
perature Tem. (cid:1) condition 4 — exclusion of semi-arid and arid
(cid:1) Dm: Day on which the imposed SDD is conditions.
achieved.
(cid:1) Lcyc:Cycle length (Dm–De).
(cid:1) Tcyc:Average temperature over the cycle. 4. Agroclimatic digital datasets
(cid:1) TmDt:Temperature Dt days after maturity.
(cid:1) TmX:Maximum temperature between Dm and Geographically referenced data from a variety
the end of the year. of sources must be used in conjunction with data
From the outputs of the program a set of provided by the ground meteorological network
digital maps were produced to show: and information obtained from satellite imagery.
(cid:1) K, emergence day — corresponding to the Tem Activities such as mapping of the potential distri-
variable taken as 10.5, 11.5, and 12.8°C; butionofpestsrelyonagroclimaticdatabasesand
(cid:1) M, length of de(cid:1)elopment — period in days, agrometeorology-applied software. GIS tools are
variable according to Tem and SDD; used for geo-statistics and spatial interpolation
(cid:1) P, temperature at the 75th day after maturity routines, e.g. for transforming point values into
— if this temperature exceeds 22°C, the dia- interpolated surfaces. As interpolated surfaces
pause is assumed to be broken and the insect must represent the ground truth in great detail,
will be killed by low winter temperatures; several techniques are used in order to ‘help’ the
(cid:1) RES, map of suitability — identification of the interpolation of the selected variable to obtain a
potential geographic distribution of D. (cid:1)ir- continuous surface with the highest resolution. To
gifera, showing HatchN values. achieve this goal, remote sensing imagery like
As an example, the suitability map of D. (cid:1)ir- DEMs, NDVIs, CCDs are used. At present, three
gifera is shown in Fig. 2. The mapping of HatchN sets of digital maps are produced.
Fig. 1. Relationship between the air temperature and features of the biological cycle of D. (cid:1)irgifera.
M. Bernardi/Acta Tropica79(2001)21–34 27
Fig.2.ThesuitabilitymapofD.(cid:1)irgifera.Themappingofminimumhatchrate(HatchN),computedbytheWoodsonandGustin
(1993)equation,givingthehatchrateasafunctionoftemperatureanddurationofexposuretothetemperature.Themappedvalues
were derived based on conditions that were unsuitable or low, moderate or highly suitable.
4.1. World climate classification maps rainfall and the averages for temperature and
sunshine were derived from the monthly values.
The global climate maps presented here are
based on data for mean monthly values of tem- 4.2. Global en(cid:1)ironmental risk assessment
perature, precipitation, and cloudiness prepared
in 1991 by R. Leemans and W. Cramer and Various digital maps as produced by other
published by the IIASA. The IIASA data corre- FAO departments are transformed into WINDISP
format and processed by SDRN to obtain the
spond to an imaginary ‘net’ covering the earth’s
global digital map showing environmental con-
surfacewithameshsizeof0.5°.Thisisequivalent
straints on crop production. The map (Fig. 4) is
to about 60 km — an area of about 3600 km2 —
obtained through a pixel-by-pixel classification
attheequator.Monthlyclimaticdataforeachcell
process of global digital maps of eight relevant
in the net are provided by weather stations and
factorsataresolutionjustunder5minutes(about
consist of an average, or ‘normal’, value of
10 km at the equator, less at higher elevations):
monthly climate elements. Using spatial extrapo-
(cid:1) rainfall variability index, based on some 20,000
lation a value is computed for each cell based on
station time series of monthly rainfall;
the neighboring station values.
(cid:1) temperature variability index;
From the original IIASA dataset, two other
(cid:1) average temperature of the coldest month;
products have been derived: a ‘climate classifica-
(cid:1) climatic biomass production potential, i.e. the
tion’ according to Ko¨ppen (1936) (Fig. 3) and the
highest amount of biomass that can be pro-
‘potential biomass’ according to Lieth (1972). The
duced under the prevailing rainfall and temper-
IIASA database includes three key climatic ele- ature constraints;
ments: the average monthly rainfall total, average (cid:1) terrain slope, as steep slopes cannot be
monthly temperature, and monthly average sun- cultivated;
shine. For each of the stations used in the grid- (cid:1) land degradation index;
ding exercise the data have been assembled over a (cid:1) soil suitability index;
long time-period — usually between 1961 and (cid:1) satellite-based vegetation index measuring ac-
1990 — and then averaged. Annual totals for tual living biomass.
28 M. Bernardi/Acta Tropica79(2001)21–34
Fig.3.ClimateclassificationaccordingtoKo¨ppen(1936),oneofthefirstglobalclimateclassificationsystems,derivedmainlyfrom
field observations on vegetation communities.
Fig. 4. Global digital map showing environmental constraints on crop production.
M. Bernardi/Acta Tropica79(2001)21–34 29
Fig.5.DigitalmapoftheaveragelengthofthegrowingperiodforAfrica,calculatedusingthemonthlymeandataforrainfalland
PET values from the FAO climatological database.
4.3. A(cid:1)erage rainfall and a(cid:1)erage potential e(cid:1)apo- and PET) were used to create the digital map of
the average length of the growing period (Fig. 5).
transpiration according to Penman maps
The Average Rainfall map (RAIN) and the
5. Spatial interpolation
Average Potential Evapotranspiration (PET) map
containdigitalimagesofthedekadalandmonthly
Originally, the spatial interpolation SEDI rou-
average values (normals) of rainfall (mm) and
tine (Hoefsloot, 1995) was developed to assist the
PET (mm) for Africa in WINDISP format, at a
interpolation of rainfall data gathered at station
resolution of 7.6×7.6 km. They were created by
performing a spatial interpolation of values at level by using METEOSAT-derived CCD images.
point level (meteorological station) using the At a later stage, the method was applied to other
Satellite Enhanced Data Interpolation (SEDI) parameters as well, such as PET, altitude, crop
routine included in WINDISP. From the FAO yields and NDVI.
climatological database the monthly mean data The concepts of this interpolation method have
for the period 1961–1990 for 1600 stations for been described at several places, although devia-
rainfall and for 2600 stations for PET have been tions from the basic idea exist. A comparable
interpolated in time by using FAOMET tools to method called ‘co-kriging’ is applied mainly to
obtain dekadal data. The two datasets (RAIN geology and ground-water studies.
30 M. Bernardi/Acta Tropica79(2001)21–34
SEDI is a simple and straightforward method 3. multiplying the grid with the image to obtain
for ‘assisted’ interpolation. The method can be the estimated image.
applied to any parameter of which the values are In step 1 values are extracted from the image
available for a number of geographical locations, and the ratio is calculated. For every point value
as long as a ‘background’ field is available that in the input rainfall data a value can be extracted
has a negative or positive relation to the parame- from the CCD image. The SEDI method will find
ter that needs to be interpolated. Three require- the pixel that coincides with a rainfall station and
ments are a prerequisite for the successful extract the pixel value. In some cases the value of
application of the SEDI method: one pixel does not give satisfactory results. There-
(cid:1) Availability of the parameter to interpolate as fore, the SEDI routine allows us to extract the
point data at different geographical locations valuesofmorethanonepixelfromtheimage,and
take its average as the image value for the station.
(e.g. rainfall, PET, crop yields).
For every station we now have a rainfall value
(cid:1) Availability of a background parameter in the
andaCCDvalue.TheSpearmanrankcorrelation
form of a regularly spaced grid (or field) for
coefficient (using the rainfall/CCD data pairs)
the same geographical area (e.g. CCD, NDVI,
yields a positive value. This means the relation-
altitude).
ship between rainfall and CCD is positive, which
(cid:1) Relation between the two parameters (negative
is to be expected. In case of a negative relation-
or positive: rainfall/CCD is positive, PET/alti-
ship, the ratio is calculated as follows:
tude is negative). A Spearman rank correlation
test can reveal whether a relation exists and Stationvalue
.
how strong this relation is. Highestpossiblepixelvalue−pixelvalue
An explanation of SEDI, using rainfall and
In step 2 a grid from the irregularly spaced
CCD as input, is now given. Rainfall measure-
ratio is created. The ratio grid is created with the
ments from ground stations are gathered at the
inverse distance method with a weighting power
central level and plotted on a map to indicate
of 2. The routine allows us to set:
point values during a given time for a particular
(cid:1) The distance between the grid lines. A low
area. In order to draw the rainfall isolines (iso-
distance creates an accurate, dense grid while a
hyets), several spatial interpolation techniques
high value creates a coarse, less accurate, and
(manual or computer-based) can be used.
more general grid.
SEDI makes it possible to drive the spatial
(cid:1) The number of stations per grid-point, which
interpolation by using the relationship with CCD.
determines the number of stations included in
The geo-stationary METEOSAT satellite pro-
the calculation of a point in the grid matrix.
duces infrared temperature images every 1/2 h. In
(cid:1) The maximum radius for interpolation, which
tropical regions it can be assumed that the areas
determines whether a value is calculated for a
with temperatures lower than about −40°C are point in the grid matrix. If the number of
covered with rain clouds. The cumulative number stations around this grid-point within this ra-
of hours in a given period (i.e. 10 days) with this dius is higher than the specified number of
low temperature is defined as CCD and it can be stations, a value is calculated. Otherwise, to
represented as a digital image. The relationship the grid-point is assigned a missing value, and
between rainfall and CCD is positive; in other the resulting image will be ‘empty’ at that
words, high rainfall values generally coincide with particular point.
high CCD values. The SEDI process is done in The last step 3 encompasses the creation of the
three steps: SEDI image. By multiplying the grid (step 2) with
1. extracting values from the image and calculat- the background image an estimate for the value
ing the ratio of point and image values; to interpolate is obtained. In terms of rainfall and
2. gridding the ratios to form a regularly spaced CCD a rainfall image is obtained by multiplying
grid; the ratio grid with the background image.