Available data for crop modelling
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Parker, L. Navarro-Racines, C. Available data for crop modelling and applications using EcoCrop. Second training in Climate vulnerability analysis using the EcoCrop model, organized by Mozambique Institute of Agricultural Research (IIAM) and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Speaker and mentor. August – September 2014, Maputo-Mozambique.
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Available data for crop modelling
- 1. L. Parker & C. Navarro 01/08/14, Maputo-Mozambique Available Data For Crop Modelling
- 2. Part I Historical Data ¿Where we can get climate information?
- 3. 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
- 4. 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 resolution 4. Geographical cover is not enough 5. Few variables (only temperature, precipitation). We need other in agriculture.
- 5. 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 – ……. Lessimportance Morecertainty The demand – Certainty Climate & Agriculture
- 6. Stations per variable • 47,554 precipitación • 24,542 tmean • 14,835 tmax y tmin -30.1 30.5 Mean annual temperature (ºC) 0 12084 Annual precipitation (mm) Fuentes: •GHCN •FAOCLIM •WMO •CIAT •R-Hydronet •Redes nacionales http://www.worldclim.org/
- 7. Algorithm of interpolation includes Latitude, longitude, elevation as covariates. As High as 1km Chicualacuala Xai Xai Chicualacuala Xai Xai http://www.worldclim.org/
- 11. CRU-TS CRU-TS v3.22 Historic Climate Database for GIS Harris et al. (2014) Label Variable cld cloud cover dtr diurnal temperature range frs frost day frequency pre precipitation tmp daily mean temperature tmn monthly average daily minimum temperature tmx monthly average daily maximum temperature vap vapour pressure wet 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
- 13. Annual Precipitation Patterns & Stations (WorldClim CA) CIAT GHCN FAO WMO Fonts And for Mozambique?? Lets view in ArcGIS
- 14. GHCN Global Historical Climatological Network • Very robust weather station dataset (NOAA) • Used for many studies: – WorldClim – CRU datasets – Hockey-stick warming trend analysis
- 15. GHCN (Global Historical Climatological Network) http://gis.ncdc.noaa.gov/map/viewer
- 16. 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)
- 17. GSOD (Global Summary of Day) Viewer link
- 19. TRMM Tropical Rainfall Measuring Mission TRMM 3B43 Characteristics Temporal Coverage Start Date: 1998-01-01; Stop Date: - Geographic Coverage Latitude: 50°S - 50°N; Longitude:180°W - 180°E Temporal Resolution Monthly Horizontal Resolution 0.25° x 0.25°; nlat = 400, nlon = 1440 Average File Size Compressed: ~4.95 MB; Original: ~4.95 MB File Type HDF Resolución espacial (~ 28 km), TRMM tiende a sobreestimar precipitación real (aunque la distribución espacial de la precipitación es bastante bueno).
- 20. TRMM TRMM Product 3B43 (V7) http://disc.sci.gsfc.nasa.gov/
- 21. 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 America J. Rozante and D. Moeira, 2010
- 22. Part II Future Data ¿Where we can get climate information?
- 23. Ramírez-Villegas and Challinor, 2012 What options we have?
- 24. Climate Modeling; Climate Change & Agriculture ( T O M O R R O W ) Carlos Navarro J. Ramirez, A. Jarvis, S. Gourdji
- 25. Part III Agricultural Data ¿Where we can get climate information?
- 26. MapSpaM The Spatial Production Allocation Mode
- 27. MapSpaM The Spatial Production Allocation Mode http://mapspam.info/data/
- 28. Growing Season Data: provided by Sacks et al (2010) Reference Sacks, 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
- 29. CASSAVA Corn Ecocrop: Climatic and soil requirements for crops http://ecocrop.fao.org/ecocrop/srv/en/home
- 30. FAOSTAT: Vast source of Country level Agricultural data. http://faostat.fao.org/
- 31. DIVAGIS: Spatial Data for National and Subnational Analysis and Mapping http://www.diva-gis.org/
- 32. Protected Planet: Location of Protected Areas in GIS Format (Available for Download) http://www.protectedplanet.net/
- 33. Spatial Data: Cities with historical and projected population statistics (provided by nordpil) https://docs.google.com/spreadsheets/d/1Vkn3kKmecbqmSycc9jRAaUC_4R7KPLcBoBRis1LFk-0/edit#gid=936077830
- 34. GeoNetwork: Global raster data for land use, agriculture, population etc http://www.fao.org/geonetwork/srv/en/main.home
- 35. AfriCover: Agriculture, landuse, elevation data for selected nations in Africa http://www.fao.org/geonetw ork/srv/en/main.home http://www.glcn.org/activitie s/africover_en.jsp
- 36. 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.polic ysupport.org/srtm
- 37. IUCN Red List: Spatial data for endangered species http://maps.iucnr edlist.org
Editor's Notes
- Las mediciones de tiempo para un sitio determinado menudo no están disponibles debido a que (1) no hay ninguna estación meteorológica, (2) las estaciones meteorológicas no están en buen estado para que los datos son o bien sólo está disponible por un corto período, o contienen lagunas, (3) Los datos recogidos no son correctamente almacenados, (4) los datos no pasan los controles de calidad básicos, y / o (5) el acceso a los datos está restringido mediante la celebración de las instituciones (Fig. 1). Esto limita aún más los análisis de impacto agrícola, destacando la importancia de que los datos públicos. Aparte de las limitaciones relacionadas con el acceso y la ubicación de las estaciones meteorológicas, probablemente la cuestión más importante en relación con los datos de clima es la calidad (Begert et al, 2008;. DeGaetano, 2006) (Fig. 1), que también afecta en gran medida el rendimiento de los modelos de impacto. Por lo tanto, la comunidad del clima y la agricultura se ha centrado en parte en el desarrollo de métodos, ya sea para llenar lagunas de datos temporal o espacial, y en el uso de estos métodos para el desarrollo de conjuntos de datos mundiales o regionales con acceso público (Hijmans et al, 2005;. Jones y Thornton, 1999; Soltani et al., 2004). Sin embargo, las incertidumbres en los conjuntos de datos globales derivados de los métodos de interpolación han sido apenas (si en absoluto) estimado (Buytaert et al, 2009;. Challinor y Wheeler, 2008;. Soria-Auza et al, 2010). Los investigadores que utilizan conjuntos de datos globales y cualquier estación meteorológica fuente Fig. 1. deben ser conscientes de estos problemas y debe tener esto en cuenta al probar la sensibilidad de sus enfoques a los problemas de exactitud (es decir, falta de homogeneidad, discontinuidades) y (si es posible) que proporcionan resultados dentro del rango de incertidumbre en los datos de entrada (es decir, como la salidas de los métodos de interpolación validados cruz) (Challinor et al., 2005).
- En los últimos 10 años, combinación de datos de estaciones meteorológicas, datos de satélite y modelos de predicción numérica del tiempo, además de los métodos de interpolación, o en la planta aplicación de modelos climáticos. El uso de estos conjuntos de datos para los propósitos de modelado agrícolas es bastante limitado para una o más de las siguientes razones: Aparte de las limitaciones relacionadas con el acceso y la ubicación de las estaciones meteorológicas, probablemente la cuestión más importante en relación con los datos de clima es la calidad, que también afecta en gran medida el rendimiento de los modelos de impacto.
- Agriculture is a niche based activity, and then we need climate data to characterize the niche. In relation to climate and agriculture, agriculture demands to multiple variables like precipitation, temperature, wind speed, soil moisture, solar radiation, relative humid, among many others. Agriculture demands very high spatial resolution, maybe 1km, ninty meters.. Also, agriculture needs a Mid-high temporal resolution. We need at least montly climate data and for some application we need daily data for example mechanistic crops models .. Both for present and future For adaptation plans we need high certainty.. Mainly for precipitation
- Superficies mensuales para prec, tmean, tmin y tmax. Compilación de registros nivel, local, regional, nacional. Interpolación 1km usando Latitud, longitud, elevación como variables independientes Calidad.. Depende de ρ y topografía GHCN (Global Historical Climatology Network), FAOCLIM, WMO Climatological Normal (CLINO) Centro Internacional de Agricultura Tropical (CIAT, ) R-Hydronet
- Superficies mensuales para prec, tmean, tmin y tmax. Compilación de registros nivel, local, regional, nacional. Interpolación 1km usando Latitud, longitud, elevación como variables independientes Calidad.. Depende de ρ y topografía GHCN (Global Historical Climatology Network), FAOCLIM, WMO Climatological Normal (CLINO) Centro Internacional de Agricultura Tropical (CIAT, ) R-Hydronet
- http://www.ipcc-data.org/ddc_climscen.html
- http://www.ipcc-data.org/ddc_climscen.html
- Daily Observational Data: GHCN Daily Summary – GIS Data Locator GHCN (Global Historical Climatology Network)-Daily is a data set whose aim is to address the need for historical daily records over global land areas. Like its monthly counterpart (GHCN-Monthly), GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The meteorological elements measured for the data set include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. GHCN-Daily serves as the official archive for daily data from the Global Climate Observing System (GCOS) Surface Network (GSN) and is particularly well suited for monitoring and assessment activities related to the frequency and magnitude of extremes. Sources for the GHCN-Daily data set include, but are not limited, to U.S. Cooperative Summary of the Day, U.S. Fort data, U.S. Climate Reference Network, Community Collaborative Rain, Hail and Snow Network, and numerous international sources. GHCN Daily Observations - GIS Data Locator The Observations map displays current and historical weather observations for six primary variables (maximum temperature, minimum temperature, average temperature, precipitation, snowfall, and snow depth). The source of the data is GHCN-Daily. Monthly Observational Data: GHCN–D Monthly Summaries – GIS Data Locator The GHCN-Daily was developed to meet the needs of climate analysis and monitoring studies that require data at a sub-monthly time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). It also serves as NCDCs sole source of U.S. Summary of the Day data, providing a diverse array of users in the public and private sector with weather and climate observations that meet needs from the local to national level. By bringing together contributions from dozens of national and international sources and combining historical with near real-time observations, this dataset helps users understand todays climate and how it impacts society while helping users prepare for weather and climate conditions in the future.
- Issues Son datos ppalmente de estaciones en aeropuerto y no reporta correctamente valores 0 de precipitacion Del potencial de estaciones hay muy pocas que reporta el NCDC Global Surface Summary of the Day is a product produced by the National Climatic Data Center (NCDC), and is derived from the synoptic/hourly observations contained in the Integrated Surface Hourly (ISH) dataset (DSI-3505). The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries, and over 9000 worldwide stations' data are available. Daily elements (as available) include mean values of temperature, dew point, sea level and station pressures, visibility, and wind speed plus maximum sustained wind speed and/or wind gusts, maximum and minimum temperature, precipitation amounts, snow depth, and indicators for occurrences of various weather elements. Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. Daily extremes and totals--maximum wind gust, precipitation amount, and snow depth-- only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements appear less frequently than other values. Since these elements are derived from the original synoptic/hourly data as are reported and based on Greenwich Mean Time (GMT, 0000Z-2359Z), they often comprise a 24-hour period which includes a portion of the previous day (i.e., offset from local standard times).
- http://www.ipcc-data.org/ddc_climscen.html
- “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”