This map of the Precipitation Change Severity Index for Central America, Haiti and the Dominican Republic during 2011 depicts the areas where changes were more severe in terms of increased precipitation, compared to the historical norm. A bar graph shows the percentage of areas with no change, very low, low, medium, high, and very high (or severe) change in rainfall during 2011 when compared to a historical baseline. These levels are color-coded, with the colors corresponding to the same level of severity on the map and on the bar graph. Data sources: Worldclim and TRMM TMPA-RT (Daily Accumulated Rainfall).

The growing complexity of the relationship between climate information and agricultural decision-making necessitates the development of relevant and timely climate services for farmers. These services can effectively support risk management strategies in agriculture by fostering a comprehensive understanding of the intricacies involved in farmer decision-making dynamics. This paper addresses this critical gap by analyzing the drivers influencing decision-making processes that shape adaptation strategies for staple grain and coffee farming systems in Central America. The study answers the following research questions: (i) Does the mind map tool effectively provide a holistic understanding of farmers' decision-making processes? (ii) How do Central American farmers make decisions within their farm systems at multiple timescales? (iii) Which climate factors trigger these decisions? Employing a combination of systematic literature review and a case study in Honduras, the study identifies 13 critical decisions farmers make throughout their crop cycle and their respective triggers. These decisions were grouped into three clusters (production, household, and environmental) and classified into lead-time categories (operational, tactical, and strategic). Findings reveal that farmers base their decisions regarding future climate expectations on their traditional knowledge, religious dates, and memories of recent past seasons' rainfall patterns, and that one of the most significant factors influencing farmers' decisions is food security shortages resulting from extreme events. For example, recent mid-summer droughts have led farmers to prioritize sowing beans over maize in the Primera season, while during the Postrera season, they face challenges due to excess rainfall and the hurricane season. We conclude that the mind map tool developed in this paper provides an effective and appropriate method and that the variation in farmers' decision-making complexity across systems and landscapes presents a significant opportunity to design mind maps that span multiple timescales, facilitating the exploration of decision spaces. Farmers actively seek tailored weather and climate information while still valuing their existing experience and local knowledge, emphasizing the importance of integrating these elements into the development of climate services.

This map shows the precipitation change in millimeters projected to occur by the midcentury (2050s) based on a median ensemble analysis of 16 General Circulation Models (GCM) downscaled to a 0.5 degree resolution (Maurer, 2009). Half of the models project a greater amount of change, and half of the models project less change as compared to the 1961-1990 baseline average. We used output from each GCM runs of the A1B greenhouse gas emissions scenarios (Nakicenovic, 2000). All projections were generated for the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset (Meehl, 2007) and used for analyses included in the IPCC Fourth Assessment Report (IPCC, 2007). These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas. Terrestrial ecoregion source: Olson, D.M., E. Dinerstein, E.D. Wikramanayake, N.D. Burgess, G.V.N. Powell, E.C. Underwood, J.A. D'Amico, I. Itoua, H.E. Strand, J.C. Morrison, C.J. Loucks, T.F. Allnutt, T.H. Ricketts, Y. Kura, J.F. Lamoreux, W.W. Wettengel, P. Hedao, and K.R. Kassem. 2001. Terrestrial ecoregions of the world: New map of life on earth. Bioscience 51(11):933-938.

Climate data sources: Maurer EP, Adam JC, Wood AW (2009) Climate model based consensus on the hydrologic impacts of climate change to the Rio Lempa basin of Central America. Hydrology and Earth System Sciences 13: 183–194. Nakicenovic N, Alcamo J, Davis G, de Vries D, Fenhann J, et al. (2000) Special Report on Emissions Scenarios. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change; Intergovernmental Panel on Climate Change, editor. Cambridge, UK.: Cambridge University Press. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, et al. (2007) The WCRP CMIP3 multimodel dataset - A new era in climate change research. Bull Amer Meteorol Soc 88: 1383–1394. IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M et al., eds. Cambridge, United Kingdom and New York, NY, USA.: Cambridge University Press. 996 p.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

This dataset present the Drought Atlas for Argentina, developed as part of the Latin American and Caribbean Drought Atlas. The maps show the minimum (and maximum) precipitation for different return periods (in years) and the frequency of drought occurrences (precipitation deficits with respect to the normal annual precipitation) for Argentina.

The State of Louisiana experienced widespread flooding during the extreme rainfall events of March and August 2016. The City of Central, Louisiana, which lies above the confluence of the Amite and Comite Rivers, is bordered on the East and West respectively by these rivers. The city had extensive damage from both events, in particular the August 2016 flood in which the river basins received up to 30 inches of documented rainfall. Many streamgages in the area recorded peak of record flood levels from the event. The US Geological Survey (USGS) in cooperation with the City of Central, created a digital flood inundation map library to depict estimated areal extents and depth of flooding along 14.5 and 20.2 mile reach lengths of the Amite and Comite Rivers. The maps were created using a 2-dimensional flow model calibrated to the March and August 2016 events as well as to the current stage-discharge ratings at USGS streamgaging stations 07377300 Amite River at Magnolia, Louisiana and 07378000 Comite River near Comite, Louisiana. The maps range from flood stage to the peak of record stage at the gaging stations. Annual peak flow data was analyzed to determine multiple flooding scenario possibilities between the two gages. This data release provides the ArcGIS files and metadata for these maps. In addition, the maps will be hosted by the USGS on an interactive web mapper accessible to the cooperator and the public at: https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program Use of the maps aids city officials and emergency managers in pre-planning for a flood event in areas such as road and bridge closures, staging of man power and materials, and estimation of affected population. The maps also aid the public in foreseeing their flood risk potential and helps them in their decision making regarding life and property.

This dataset contains over 14,000 hours of regional radar mosaics over the northeast US from 600+ winter storm days between 1996-2023. Winter storm days are defined when at least 2 out of 15 surface stations in the northeast US (see attached map) produced at least 1 inch of snow over the 24 hour period. Sequences of these mosaics aid in analyzing the precipitation area and the structures within winter storms. Radar reflectivity data is combined from the first, lowest (0.5 degree) elevation angle from 12 NEXRAD WSR-88D radars in the northeast US (see attached). The scans occur every 5-10 minutes from each radar depending on the radar scan settings. The time label of the regional map is based on the scan time central radar, KOKX (Upton, NY). Scans from other radars in the region are used for that time as long as they are within 8 minutes of the KOKX scan. The polar radar data from each radar is interpolated to a regional 1202 km x 1202 km Cartesian grid with 2 km grid spacing covering 35..., , , # Regional NEXRAD radar mosaics of winter storms from 1996-2023, part 1

This dataset contains over 14,000 hours of regional radar mosaics over the northeast US from 600+ winter storm days between 1996-2023. Winter storm days are defined when at least 2 out of 15 surface stations in the northeast US (see attached map and .csv) produced at least 1 inch of snow over the 24 hour period. Sequences of these mosaics aid in analyzing the precipitation area and the structures within winter storms. Radar reflectivity data is combined from the first, lowest (0.5 degree) elevation angle from 12 NEXRAD WSR-88D radars in the northeast US (see attached). The scans occur every 5-10 minutes from each radar depending on the radar scan settings. The time label of the regional map is based on the scan time central radar, KOKX (Upton, NY). Scans from other radars in the region are used for that time as long as they are within 8 minutes of the KOKX scan. The polar radar data from each radar is interpolated ...

This data set consists of 83 digital maps that were produced by the Food and Agriculture Organization of the United Nations (FAO) for the World Bank as part of a Global Farming Systems Study. The maps are distributed through the FAO-UN GeoNetwork Portal to Spatial Data and Information.

As part of the World Bank's review of its rural development strategy, the Bank sought the assistance of FAO in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and a global overview. The global overview, which synthesizes the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. The global document is supplemented by two case study reports of development issues of importance to farming systems globally.

The six regions studied include:

East Asia Pacific East Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia Sub-Saharan Africa

Map coverages for each region include the following:

Average precipitation Average temperature Elevation Irrigation intensity Land cover Length of growing period Livestock stocking density Major environmental constraints Major farming systems NOAA Satellite imagery (shaded relief imagery and ocean floor bathymetry) Permanent crop and arable land Rural population Slope Total population

The map coverages were prepared by FAO based on the following data sources:

Doll, P. and Siebert, S. 1999. A Digital Global Map of Irrigated Areas, Report No A9901, Centre for Environmental Systems Research, University of Kassel, Kassel, Germany.

Environmental Systems Research Institute (ESRI) Data and Maps 1999, Volume 1. World Worldsat Color Shaded Relief Image. Based on 1996 NOAA weather satellite images, with enhanced shaded relief imagery and ocean floor relief data (bathymetry) to provide a land and undersea topographic view. ESRI, Redlands, California, USA.

Food and Agriculture Organization of the United Nations (FAO), Land and Water Development Division (AGL) with the collaboration of the International Institute for Applied Systems Analysis (IIASA). 2000. Global Agro-Ecological Zones Study. FAO, Rome, Italy.

Gomes, R. 1999. Major Environmental Constraints for Agricultural Production Project. Based on FAOCLIM database, ARTEMIS NDVI imagery, and soil and terrain data provided by Soil Resources Management and Conservation Service. FAO-GIS. Food and Agriculture Organization of the United Nations (FAO), Environment and Natural Resources Service, Rome, Italy.

Leemans, R. and Cramer, W. 1991. The IIASA Database for Mean Monthly Values of Temperature, Precipitation and Cloudiness on a Global Terrestrial Grid. Research Report RR-91-18. November 1991. International Institute of Applied Systems Analyses, Laxenburg, pp. 61.

Oak Ridge National Laboratory, LandScan Global Population 1998 Database. Oak Ridge National Laboratory (ORNL), Oak Ridge, Tennessee, USA.

Slingenbergh, J. Livestock Distribution, Production and Diseases: Towards a Global Livestock Atlas. Food and Agriculture Organization of the United Nations (FAO), AGAH, Rome, Italy. (aka Global Livestock Production and Health Atlas (GLiPHA))

U.S. Geological Survey, EROS Data Center. 1996. GTOPO30 Digital Data Set. EDC, Sioux Falls, South Dakota, USA.