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Development of New Climate and Plant Adaptation Maps for China Proc., 12th AMS Conf. on Applied Climatology, Amer. Meterological Soc., May 8-11, 2000 JP1.23 DEVELOPMENT OF NEW CLIMATE AND PLANT ADAPTATION MAPS FOR CHINA Christopher Daly *, Wayne Gibson, David Hannaway, and George Taylor Oregon State University, Corvallis, OR 97331, USA 1. INTRODUCTION climate mapping expertise, makes it possible to spatially verify and extrapolate the results of With the rapid and continuous economic these field trials to all of China. Climate growth in China, market demands for improved mapping, in combination with spatial soils data forage-livestock systems, urban beautification, and climatic tolerances of grass species, can be and improved environmental protection - used to produce very detailed maps of species including soil conservation and erosion control - adaptation that can be used to accurately identify have greatly expanded in the last decade. suitable growing areas for effective marketing. Specifically, the increased demands for animal products, beautiful turf for roadsides, lawns, and The objectives of the work described here are to: golf courses, and reduced soil erosion in the • Obtain and quality-check observed climate Yellow (Huang) and Yangtze (Chang Jiang) data from Chinese authorities. River watersheds have contributed to increased • Prepare detailed draft maps of mean market demands for high quality grass seeds. monthly minimum/maximum temperature In 1992 the Oregon Seed Council, a and precipitation for the People’s Republic consortium of over 50 grass seed companies, of China. partnered with Oregon State University to • Use the climate maps and expert estimates initiate a USDA Market Access Program (MAP) of species climatic tolerances to prepare project for developing the China market for adaptation maps for several important Oregon-grown grass seed. Other cooperators Oregon-grown grasses. include the Oregon Department of Agriculture, • Subject draft climate and adaptation maps to the Chinese Academy of Agricultural Sciences, peer review by Chinese climatologists, the Chinese National Meteorological Center, obtain supplementary climate data, and China Agricultural University, Nanjing revise maps as necessary. Agricultural University, Wuhan Technical University of Surveying and Mapping, Jiangsu This paper describes the methods used to Academy of Agricultural Sciences, the YiChang accomplish the first three objectives for eastern Municipality, and the Three Gorges China: obtain climate data; create first-draft Development Corporation. climate maps; and develop grass adaptation Effective marketing of our high quality US- maps. grown seeds requires that we be able to identify all of the areas suitable for using these grasses. 2. METHODS Until now that has been impossible on a wide scale. The USDA MAP project has conducted 2.1 Collection and quality control of field-based trials in China. However, these have observational data been few in number and located mostly in the populated eastern lowlands, because of the In 1998, contact was established with the difficulty in finding suitable cooperators, Chinese Climate Data Center (CDC), which is a establishing and maintaining test plots, and part of the Chinese National Meteorological collecting accurate data on species performance Center in Beijing. The CDC is responsible for over a period of years. the collection, archive, and dissemination of Fortunately, GIS technology, combined with climate data for the national climatic network, * Corresponding author address: Christopher Daly, Director, Spatial Climate Analysis Service, Dept. of Geosciences, Oregon State University, 326 Strand Agricultural Hall, Corvallis, OR 97331; e-mail: [email protected] Proc., 12th AMS Conf. on Applied Climatology, Amer. Meterological Soc., May 8-11, 2000 which consists of approximately 700 stations. In checked for reasonableness by applying exchange for seminars at CDC by Daly and ASSAY, a version of PRISM (see below) that Hannaway, and a three-week training course for performs jackknife cross-validation on each two of their scientists at Spatial Climate station in the dataset. Stations with large Analysis Service offices at Oregon State observation-prediction errors were flagged. The University, the CDC provided us with mean PRISM Graphical User Interface was used to monthly minimum and maximum temperature display climate-elevation scatterplots in the and precipitation for 679 stations. These vicinity of the questionable stations to determine stations have the longest and most complete if the station “fit in” with the others, or was an records available. obvious outlier. Outliers were removed from the Methods used by the CDC to quality-control dataset. In general, few monthly outliers were the station data were unknown. Therefore, we found, giving us confidence that the Chinese had to assume that errors existed in the data. climate data were of good quality. Two types of checks were made: metadata and monthly data. The location and elevation 2.2 Preparation of Climate Maps metadata were checked by plotting each station on a 30-second digital elevation model (DEM) Spatial modeling of the climate data was from the ETOPO-30 global elevation dataset. If performed at 2.5-minute (~ 4-km) resolution. A the station elevation differed substantially from 2.5-minute DEM was derived from the ETOPO- the elevation of DEM pixels in the immediate 30 DEM series. GIS was used to prepare vicinity, it was assumed that either the DEM was supplementary grids used in the interpolation. incorrect or of insufficient resolution, or the These included a coastal proximity grid, reported station location or elevation were estimates of wintertime temperature inversion faulty. Many DEM-station elevation heights, and an effective terrain grid. discrepancies were found. In some regions, such The PRISM (Parameter-elevation as the deeply dissected terrain of the Regressions on Independent Slopes Model) southeastern Tibetan Plateau, the limited knowledge-based system (Daly et al., 1992, precision of the station location (nearest 1 arc- 1994, 1997, in review, Daly and Johnson 1999) minute) and the inability of the 30-sec DEM to was used to grid precipitation and minimum and resolve locations at the bottoms of deep canyons maximum temperature over China. PRISM were the culprits. An atlas was used in attempts technology is being used in several climatic to locate the place names and corroborate station mapping efforts in the US, including a 103-year locations. Stations that could not be found on an climate time series (Daly et al., 1999), a major atlas and had clearly unreasonable elevations precipitation mapping effort for NRCS (USDA- were brought to the attention of CDC personnel NRCS, 1998), an updated climate atlas for the and attempts made to resolve or correct the NCDC, and others (e.g., Johnson et al., in press). discrepancies. PRISM uses point data, a DEM, and other As an additional check, the station metadata spatial data sets to generate estimates of climatic were compared to those for the same stations in elements that are gridded and GIS-compatible. the Global Historical Climate Network, prepared and maintained by the US National Climatic The strong variation of climate with Data Center (NCDC). After lengthy discussions elevation is the main premise underlying the with NCDC, the most common source of model formulation. PRISM adopts the metadata discrepancies was found to be station assumption that for a localized region, elevation moves, which caused the GHCN metadata, is the most important factor in the distribution of recorded some time in the past, to differ from temperature and precipitation. Observations the Chinese metadata, which reflects only the from many parts of the world show the current location of the station. No station altitudinal variations of temperature and histories were available from CDC from which precipitation to approximate a linear form. to track station moves over time. Available station data often do not span the The mean monthly station data were complete range of elevations in an area, Proc., 12th AMS Conf. on Applied Climatology, Amer. Meterological Soc., May 8-11, 2000 especially in mountainous regions. Therefore, First drafts of species adaptation maps were vertical extrapolation is required. This is prepared for three important Oregon-grown accomplished in PRISM at each DEM grid cell cool-season grasses: tall fescue, orchardgrass, (termed the target grid cell) through a moving- and perennial ryegrass. These first maps window simple linear climate-elevation accounted only for climatic tolerances and did regression. This regression function serves as not include soils considerations. The climatic the main predictive equation in the model. constraints were also simple as a starting point. Upon entering the regression function, each Each species was assigned approximate values station is assigned a weight that is based on of mean January minimum temperature, mean several factors. The combined weight W of a July maximum temperature, and mean annual station is a function of the following: precipitation that corresponded with well- adapted, moderately adapted, marginally W = f { Wd , Wz , Wc , Wl , Wf , Wp , We } adapted, and not adapted conditions, respectively. GIS was used to apply these where Wd , Wz , Wc , Wl , Wf , Wp and We are the constraints to the PRISM climate grids to distance, elevation, cluster, vertical layer, produce maps showing climatic adaptation zones topographic facet, coastal proximity, and for each species. effective terrain
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