Available data for crop modelling

L. Parker & C. Navarro

01/08/14, Maputo-Mozambique

Available Data For Crop Modelling

Part IHistorical Data

¿Where we can get climate information?

Ramírez-Villegas and Challinor, 2012

Understanding the problem(1) There are not any

meterorological station (2) The weather stations are

not good (short periods, gaps)

(3) Data are not storage properly

(4) Data doesn’t pass basic quality control

(5) Restricted access

Figure 1 Frequency of use of the different data sources in agricultural studies based on a review of 247 recordings from published studies (taken from a comprehensive data use survey) (Ramirez-Villegas and Challinor 2012)

What options we have?Exactitude problems (i.e. no homogeneity , discontinued)

1. High Time-step (monthly in the best case)

2. Temporal cover only for years average.

3. Coarse resolution4. Geographical cover is not enough5. Few variables (only temperature,

precipitation). We need other in agriculture.

Agriculture niche business– Multiple variables– Very high spatial resolution– Mid-high temporal (i.e.

monthly, daily) resolution– Accurate weather forecasts

and climate projections– High certainty

• Both for present and future

–T°• Max,• Min, • Mean

–Prec– HR– Radiation– Wind– …….

Less

impo

rtan

ce

Mor

e ce

rtai

nty

The demand – CertaintyClimate & Agriculture

Stations per variable

• 47,554 precipitación • 24,542

tmean • 14,835

tmax y tmin

- 3 0 . 1

3 0 .5

M e a n a n n u a lt e m p e r a t u r e ( º C )

0

1 2 0 8 4

A n n u a l p r e c i p i t a t i o n ( m m )

Fuentes:•GHCN•FAOCLIM•WMO•CIAT•R-Hydronet•Redes nacionales

http://www.worldclim.org/

WorldClim - MozambiqueAlgorithm of interpolation includes Latitude, longitude, elevation as covariates.As High as 1km

Chicualacuala

Xai Xai

Chicualacuala

Xai Xai

http://www.worldclim.org/

http://www.worldclim.org/

http://www.worldclim.org/

mm/month

http://www.worldclim.org/

deg celsius

CRU-TSCRU-TS v3.22 Historic Climate Database for GISHarris et al. (2014)

Label Variablecld cloud coverdtr diurnal temperature rangefrs frost day frequencypre precipitationtmp daily mean temperaturetmn monthly average daily minimum temperature

tmx monthly average daily maximum temperaturevap vapour pressurewet wet day frequency

• High Resolution Grids• 0.5 degree • Month-by-month variation in

climate over the last century or so• Latest generate over 1901-2013

http://www.cru.uea.ac.uk/ CRU High-resolution gridded datasets

Annual Precipitation Patterns & Stations (WorldClim CA)

CIATGHCNFAOWMO

Fonts

And for Mozambique??Lets view in ArcGIS

GHCN Global Historical Climatological Network

• Very robust weather station dataset (NOAA)

• Used for many studies:– WorldClim– CRU datasets– Hockey-stick warming

trend analysis

GHCN (Global Historical Climatological Network)http://gis.ncdc.noaa.gov/map/viewer

GSOD Global Summary of Day Viewer link

• Version 8 - Over 9000 Worldwide Stations - Updated Daily• Some issues Mean temperature (.1 Fahrenheit)

Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches)

GSOD (Global Summary of Day)Viewer link

http://srtm.csi.cgiar.org/ SRTM 250m Digital Elevation Data

TRMMTropical Rainfall Measuring Mission

TRMM 3B43 CharacteristicsTemporal Coverage Start Date: 1998-01-01; Stop Date: -

Geographic Coverage Latitude: 50°S - 50°N; Longitude:180°W - 180°E

Temporal Resolution MonthlyHorizontal Resolution 0.25° x 0.25°; nlat = 400, nlon = 1440Average File Size Compressed: ~4.95 MB; Original: ~4.95 MBFile Type HDF

Resolución espacial (~ 28 km), TRMM tiende a sobreestimar precipitación real (aunque la

distribución espacial de la precipitación es bastante

bueno).

TRMMTRMM Product 3B43 (V7)

http://disc.sci.gsfc.nasa.gov/

A Study Case…

“En regiones con una alta densidad de estaciones de superficie, no se encontraron mejoras significativas en el producto de combinación (donde de hecho hay poca contribución de TRMM) en simplemente la interpolación de las observaciones existentes (OBS90). Sin embargo, los análisis resultantes sobre las regiones de baja densidad de observación (al oeste de 568W) muestran sustancial mejora en el producto MERGE en comparación con OBS90. MERGE ha demostrado ser una herramienta valiosa en el análisis de una rejilla regular para su uso en la evaluación de los resultados del modelo”

Combining TRMM and Surface Observations of Precipitation: Technique and Validation over South AmericaJ. Rozante and D. Moeira, 2010

Part IIFuture Data

¿Where we can get climate information?

Ramírez-Villegas and Challinor, 2012

What options we have?

Climate Modeling; Climate Change &

Agriculture( T O M O R R O W )

Carlos NavarroJ. Ramirez, A. Jarvis, S. Gourdji

Part IIIAgricultural Data

¿Where we can get climate information?

MapSpaMThe Spatial Production Allocation Mode

MapSpaMThe Spatial Production Allocation Mode

http://mapspam.info/data/

Growing Season Data: provided by Sacks et al (2010)

ReferenceSacks, W.J., D. Deryng, J.A. Foley, and N. Ramankutty (2010). Crop planting dates: an analysis of global patterns. Global Ecology and Biogeography 19, 607-620.

http://ecocrop.fao.org/ecocrop/srv/en/home

CASSAVA Corn

Ecocrop: Climatic and soil requirements for crops http://ecocrop.fao.org/ecocrop/srv/en/home

FAOSTAT: Vast source of Country level Agricultural data. http://faostat.fao.org/

DIVAGIS: Spatial Data for National and Subnational Analysis and Mapping http://www.diva-gis.org/

Protected Planet: Location of Protected Areas in GIS Format (Available for Download)

http://www.protectedplanet.net/

Spatial Data: Cities with historical and projected population statistics (provided by nordpil)

https://docs.google.com/spreadsheets/d/1Vkn3kKmecbqmSycc9jRAaUC_4R7KPLcBoBRis1LFk-0/edit#gid=936077830

GeoNetwork: Global raster data for land use, agriculture, population etc http://www.fao.org/geonetwork/srv/en/main.home

AfriCover: Agriculture, landuse, elevation data for selected nations in Africa

http://www.fao.org/geonetwork/srv/en/main.home

http://www.glcn.org/activities/africover_en.jsp

King’s College London (KCL): Geospatial Tools and Datasets

Range of Policy Support Tools and GIS datasets are available for download. Including Costing Nature, an ecosystem based modelling tool, and Terra I the deforestation monitoring tool (but it is still focused only on S America)

http://geodata.policysupport.org/srtm

IUCN Red List: Spatial data for endangered species http://maps.iucnredlist.org