Transcript D3-Stefanski-GIS.pps - The World AgroMeteorological Information

Utilization of GIS Technology for
Agrometeorological Applications
Minimum Temperatures in Winter Wheat Areas
April 13, 2004
NE
8%
CO
18%
49%
KS
93%
OK
100%
TX
LEGEND
= Major Growing Area
= Minor Growing Area
XX = Minimum Temperature
(Source: NOAA)
XX% = Percent Crop Jointing
(Source: NASS)
World Agricultural Outlook Board
Joint Agricultural Weather Facility
Robert Stefanski and Ray Motha
World Meteorological Organization
U.S. Dept. of Agriculture
World Agricultural Outlook Board
Introduction
• Several analytical techniques are used
to monitor crop weather worldwide
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time series analyses
Historical analog comparisons
static maps
Depiction of ET, soil moisture
• Until recently, many maps were static
and depicted just one variable, making
it difficult to:
5 - WESTERN CORN BELT
Percent of Normal Precipitation: May 1 to Dec 31
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– overlay data sets
– visualize and evaluate relationships
– easily assess crop weather conditions
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• Geographic Information Systems (GIS)
have helped overcome these hurdles
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GIS Described
Simple GIS
Robust GIS
• GIS defined: method for organizing,
displaying, and analyzing spatial data
and their relationships using computers
and compatible technologies
• GIS incorporates quantitative data
directly into the system, helping users:
– overlay multiple data sets
– create precise maps
– perform spatial analyses
• Numerous organizations use GIS to
study, monitor, and model processes
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GIS Users
USDA Forest Service
GIS used to map wildfire burn severity and to
focus efforts to minimize flooding and erosion
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GIS used to map crop areas
annually for selected states
GIS used to delineate field boundaries,
map land use, and calculate acreages
GIS used to map various climatic parameters
PRISM
Parameter-elevation Regressions on Independent Slopes Model
- Generates gridded estimates of climatic parameters
- Moving-window regression of climate vs. elevation for
each grid cell
- Uses nearby station observations
- Spatial climate knowledge base weights stations in the
regression function by their climatological similarity to
the target grid cell
Station Weighting
Combined weight of a station is:
W = f {Wd, Wz, Wc, Wf, Wp, Wl, Wt, We}
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Distance
Elevation
Clustering
Topographic Facet
(orientation)
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Coastal Proximity
Vertical Layer (inversion)
Topographic Index (cold air pooling)
Effective Terrain Height
(orographic profile)
http://www.ocs.orst.edu/prism/
GIS used to track tropical cyclones
GIS used to map flooding associated
with a landfalling hurricane
WAOB GIS
• Software
– ArcView 3.x
– ArcGIS 9.x
• Hardware
– 7 Pentium IV desktop computers
• Processing speed 2.4 to 2.8 GHz
• 512 MB RAM
• Windows 2000/XP operating system
– PCs connected via local area network
– Oracle 9i database
Data
• U.S. National Weather Service
– synoptic/cooperative observer data
• WMO data important
• NWS/WMO data archived in DBMS
• Data describing extreme weather
– tropical cyclone wind/coordinate data
– mesonetwork temp./precip. data
• USDA National Agricultural Statistics
Service (NASS) crop production, yield,
and area data
• NASS weekly crop progress/condition
data
USDA Agrometeorological GIS Applications
• WAOB GIS regularly used to create a
variety of agricultural weather
analyses
• Products grouped into three categories:
– Manual, single-parameter
applications
– Automated, single-parameter
applications
– Manual, multiple-parameter
applications
Manual, Single-Parameter Applications
Whea t crop ca lendar for m ost of Russia
Russia: Wheat
PLANT
PLANT
HEAD
HEAD
HARVEST HARVEST
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Blue = w inter w he at
Pink = spr ing w heat
Northern
Region
Central
Region
15
Siberia
Region
Urals
Region
Volga
Region
22
8
26
Southern
Region
28
Legend
Percentages indicate each region's contribution
to total national production. Regions not numbered
contribute less than 1% to the national total.
Major growing areas
Minor growing areas
Area depicted in map above
Lakes
#
Major growing areas combined account for 75% of total
national production
#
Major and minor growing areas combined account for
99% of total national production
#
Major and minor growing areas and country production
percentages based upon averaged oblast-level data from
1996-2000.
Bela
rus
Uk
ra in
e
Russia
Ka za kh s tan
Source : Sove con Agr okhleb Bulle ti n Statistics and Forec asts
Febr uary 18, 20 03 Is sue No.4 (41), 200 2.
JOINT AGRICULTURAL WEATHER FACILITY (JAWF)
• Refer to those WAOB products that
map one agricultural or meteorological
parameter and are generally laborintensive to create
• Created by manually converting raw
data into GIS-compatible formats and
then using a GUI to import and display
these data in the GIS
• GUI also used to add text and legends
to the crop and weather maps, and
thus create the finished products
Whea t crop ca lendar for m ost of Russia
Russia: Wheat
PLANT
PLANT
HEAD
HEAD
HARVEST HARVEST
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Blue = w inter w he at
Pink = spr ing w heat
Northern
Region
Central
Region
15
Siberia
Region
Urals
Region
Volga
Region
22
8
26
Southern
Region
28
Legend
Percentages indicate each region's contribution
to total national production. Regions not numbered
contribute less than 1% to the national total.
Major growing areas
Minor growing areas
Area depicted in map above
Lakes
#
Major growing areas combined account for 75% of total
national production
#
Major and minor growing areas combined account for
99% of total national production
#
Major and minor growing areas and country production
percentages based upon averaged oblast-level data from
1996-2000.
Bela
rus
Uk
ra i
ne
Russia
Ka za kh s tan
Source : Sove con Agr okhleb Bulle ti n Statistics and Forec asts
Febr uary 18, 20 03 Is sue No.4 (41), 200 2.
JOINT AGRICULTURAL WEATHER FACILITY (JAWF)
Crop Production Data – Internet
U.S. Corn
Crop Production Data – Excel
U.S. Corn
Crop Production Data – ArcView
U.S. Corn
Weather Analyses – Text File
Text file, comma-delimited WMO data
Note latitude/longitude data in addition to
weather data
Weather Analyses – GIS Table
Weather Analyses – Data Plotted
Weather Analyses – Data Contoured
Automated, Single-Parameter Applications
• Similar to manual, single-parameter
applications in that one agricultural or
meteorological parameter is displayed
and analyzed on each map, however,
the process for creating these products
has been automated
• Product creation process can be time
consuming and tedious if a large
number of products are desired and
these products are created manually
• Automation significantly reduces the
time and labor required to produce
these products
Avenue Scripts
• Object-oriented programming
language, enables users to automate
various tasks associated with mapping
• Examples of automation:
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loading data
spatial analysis (e.g., contouring)
defining map scale/extent
annotation
creating a map legend
exporting/printing a map
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Daily Plot Maps
Green number = precipitation
Red number = maximum temperature
Blue number = minimum temperature
Empty, partially filled, and completely filled
green circles symbolize precipitation amounts
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Color Contour Maps
ZAM BIA
S out hern Af rica
Pe rc ent of Norm al P rec ipitation
Dec e mbe r 1, 20 01 - Dece mb er 3 1, 2 00 1
ANG O LA
ZI MBABW E
BO TS W ANA
MO ZAM BIQ UE
NAM IBIA
Northern
Pr ov ince
Mpum .
North-W es t
Northern
Cape
Ga u.
SW AZ.
Kw aZulu
Or ange
Fr ee
N atal
S tate LE SO THO
Perc ent of Normal
< 25 %
I NDIAN
25 - 50
OC EAN
50 - 75
%
%
75 - 100 %
Eas te rn
C ape
We stern
Cape
100 - 125 %
125 - 150 %
150 - 200 %
> 200 %
Prec ip. = 0
Manual, Multiple-Parameter Applications
Minimum Temperatures in Winter Wheat Areas
April 13, 2004
NE
8%
CO
18%
49%
KS
93%
OK
100%
TX
LEGEND
= Major Growing Area
= Minor Growing Area
XX = Minimum Temperature
(Source: NOAA)
XX% = Percent Crop Jointing
(Source: NASS)
World Agricultural Outlook Board
Joint Agricultural Weather Facility
• Refer to those WAOB products that
map two or more agrometeorological
parameters and are generally laborintensive to create
• Demonstrate the significant overlay
capabilities of GIS, specifically the
ability to visualize – and quantify – the
percent of agriculture affected by
various types of weather
• Often typify the special crop weather
assessments prepared by WAOB
meteorologists in response to extreme
or severe weather
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Crop Weather Analyses – Hurricane Frances
Crop Weather Analyses – Hurricane Ivan
U.S. Drought Monitor – Background
• In 1999, government and university
scientists began working together to
produce the U.S. Drought Monitor
(USDM), a weekly product designed to
provide a single snapshot of the spatial
extent and intensity of drought in U.S.
• Drought experts from four agencies are
responsible for coordinating USDM
production each week
• On a rotating basis, an individual from
one of these agencies serves as product
author for the week, and typically
authors the product for 2 weeks.
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U.S. Drought Monitor - Methodology
• Each Monday, author consults data
from numerous sources
1st draft
2nd draft
3rd draft
FINAL
1st draft
2nd draft
3rd draft
FINAL
1st draft
2nd draft
3rd draft
FINAL
1st draft
2nd draft
3rd draft
FINAL
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quantitative observational networks
model output
satellite and radar imagery
subjective reports
• Author uses these data to prepare a
first draft of the USDM for that week
• Draft distributed via email list-server
to approximately 150 people, including
fellow authors and climate and water
experts from around the country.
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U.S. Drought Monitor - Methodology
• Members of drought list provide
author feedback, used to refine USDM
• Through iterative process, author
prepares and distributes 2-3 drafts of
the USDM during Monday, Tuesday,
and Wednesday of each week to obtain
the best product possible.
• Final product and an accompanying
text summary posted every Thursday
at 0830 LT on the USDM web site:
(http://www.drought.unl.edu/dm/monitor.html)
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U.S. Drought Monitor - Software
• Originally prepared using CorelDraw
– unable to overlay indices
– quantitative analysis not possible
• USDM authors switched to ArcGIS
• Authors obtained professional training
– draw drought areas
– annotate map
– print/export product
• Initial difficulties using GIS blamed on
– author inexperience
– deadlines limiting troubleshooting time
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Thank You !