Agroclimatic Zones Map of Iran Explanatory Notes
Total Page：16
File Type：pdf, Size：1020Kb
AGROCLIMATIC ZONES MAP OF IRAN EXPLANATORY NOTES E. De Pauw1, A. Ghaffari2, V. Ghasemi3 1 Agroclimatologist/ Research Project Manager, International Center for Agricultural Research in the Dry Areas (ICARDA), Aleppo Syria 2 Director-General, Drylands Agricultural Research Institute (DARI), Maragheh, Iran 3 Head of GIS/RS Department, Soil and Water Research Institute (SWRI), Tehran, Iran INTRODUCTION The agroclimatic zones map of Iran has been produced to as one of the outputs of the joint DARI-ICARDA project “Agroecological Zoning of Iran”. The objective of this project is to develop an agroecological zones framework for targeting germplasm to specific environments, formulating land use and land management recommendations, and assisting development planning. In view of the very diverse climates in this part of Iran, an agroclimatic zones map is of vital importance to achieve this objective. METHODOLOGY Spatial interpolation A database was established of point climatic data covering monthly averages of precipitation and temperature for the main stations in Iran, covering the period 1973-1998 (Appendix 1, Tables 2-3). These quality-controlled data were obtained from the Organization of Meteorology, based in Tehran. From Iran 126 stations were accepted with a precipitation record length of at least 20 years, and 590 stations with a temperature record length of at least 5 years. The database also included some precipitation and temperature data from neighboring countries, leading to a total database of 244 precipitation stations and 627 temperature stations. The ‘thin-plate smoothing spline’ method of Hutchinson (1995), as implemented in the ANUSPLIN software (Hutchinson, 2000), was used to convert this point database into ‘climate surfaces’. These are raster-based files that are geographically referenced, contain continuous climatic values, and can be imported into a GIS system. The Hutchinson method is a smoothing interpolation technique in which the degree of smoothness of the fitted function is determined automatically from the data by minimizing a measure of the predictive error of the fitted surface, as given by the generalized cross- validation (GCV). The GCV is calculated by removing each data point and calculating the residual from the omitted data point of a surface fitted to all other data points using the same smoothing parameter value. The GCV is then a suitably weighted sum of the squares of these residuals and can be approximated (Hutchinson, 2000) as: with MSE the mean square error of the fitted function, and σ2 the error variance. Three independent spline variables were used, latitude, longitude and altitude. The latter was input to the model in the form of a DEM ASCII grid file. The DEM used to generate the climate surfaces was GTOPO30, a global DEM with 30 arc-second (approximately 1 km) resolution (Gesch and Larson, 1996). Parameter estimation was undertaken over a regular grid with the same dimensions and resolution as the user-provided DEM. In order to automate the process of climate surface generation, which is rather cumbersome, an auxiliary software product CLIMAP was used (Pertziger and De Pauw, 2002). This Excel-based software provides a user-friendly interface for running ANUSPLIN and for generating derived surfaces using CLIMAP-provided models. Using above procedure, surfaces of mean monthly precipitation, minimum, maximum and mean temperature were generated with 30 arc-second resolution. From the precipitation 1 surfaces, the surface was created of the total annual precipitation (Fig. 7). From the mean temperature surfaces the surfaces of the mean temperatures during the warmest month of the year (Fig. 8) and during the coldest month of the year were generated (Fig. 9). In addition, the temperature surfaces were used to calculate the surface of annual potential evapotranspiration (PET) according to the method of Penman-Monteith (Fig. 10). The methodology for the calculation of PET from temperature is explained in Appendix 2. Agroclimatic Zones Map These layers were integrated in accordance with the UNESCO classification system for arid zones (UNESCO, 1979). This system is based on three major criteria: • Moisture regime; • Winter type • Summer type In this classification system the moisture regime is determined by the ratio of annual rainfall over annual potential evapotranspiration, calculated according to the Penman method (Appendix 2). This ratio is also referred to as the aridity index. It is therefore particular to this system that in the definition of the moisture regime not only the water supply (precipitation) is considered, but also the water demand (evapotranspiration). Different classes may thus result from different values of the two terms. The winter type is determined by the mean temperature of the coldest month. The summer type is determined by the mean temperature of the warmest month . The ACZ-map was generated by combining moisture regime, winter type and summer type, in accordance with the classes of Appendix 3 (Fig. 1; map in Fig.12; Tables 5-7). Fig. 1. Combination of basic climate surfaces into agroclimatic zones The classes were created using a program written in Quickbasic (Appendix 4), an old version of Basic that can easily be translated into new Basic versions, such as Visual Basic. The output file was imported into ArcView for combining with other layers, such as population centers and roads. 2 Originally designed for the differentiation of arid zones, the system has been extended to include also the more humid climates. For example the moisture regime ‘Per-humid’ (aridity index >1) has not been defined in the original system, but has been added here in order to provide a better differentiation within the more humid zones and allow extension into areas not covered by the original UNESCO map. This extension allows the UNESCO classification to be used at a global level. In this new global applicability it matches the Köppen climatic system (Köppen and Geiger, 1928). There are several reasons why for this particular exercise the UNESCO system was preferred above the Köppen climatic system. Firstly, it is a classification specifically designed for the characterization of dryland climates. In addition, the meaning of each class is easier to understand than the classes of the Köppen system. As the classes are validated by the actual existence of the combinations of moisture regime, winter and summer type, the system is open-ended and does not require a-priori knowledge about the existence of particular climates. Hence, it is perfectly possible that some combinations do not exist in reality. A final advantage is that the UNESCO system is sufficiently flexible to be used at different scales, from the global to the local. It allows differentiation of more narrowly defined classes, making it suitable for climatic zoning at basin or catchment level, particularly if strong altitudinal gradients exist. RESULTS The following maps and GIS` layers have been produced as part of the Agroclimatic Zones mapping: • Annual Precipitation • Annual Potential Evapotranspiration (Penman-Monteith)_ • Annual Aridity Index • Mean Temperature of the Warmest Month • Mean Temperature of the Coldest Month • Agroclimatic Zones These maps are shown in Appendix 5 (Figs. 7-12). On the basis of the three criteria, moisture regime, winter type and summer type, a total of 28 Agroclimatic Zones has been differentiated, of which only six (A-C-W, A-C-VW, A-M-VW, SA-K-W, SA-C-W, and SA-K-M) occupy nearly 90% of Iran (Table 1). 3 Table 1. Agroclimatic zones of Iran and their extent Temperature Temperature Moisture % of approx. area Symbol regime regime regime country (sq.km) summer winter HA-M-VW Hyper-arid Mild Very warm 2.5, 41,647 HA-C-VW Hyper-arid Cool Very warm 0.2 3,687 A-M-VW Arid Mild Very warm 16.7 286,822 A-M-W Arid Mild Warm 0.6 9,705 A-C-VW Arid Cool Very warm 18.7 305,814 A-C-W Arid Cool Warm 26.2 429,257 A-C-M Arid Cool Mild 0.0 11 A-K-W Arid Cold Warm 2.3 36,485 A-K-M Arid Cold Mild 0.2 2,758 SA-M-VW Semi-arid Mild Very warm 0.3 5,380 SA-C-VW Semi-arid Cool Very warm 1.6 26,454 SA-C-W Semi-arid Cool Warm 7.3 11,7526 SA-C-M Semi-arid Cool Mild 0.0 8 SA-K-W Semi-arid Cold Warm 17.2 271,593 SA-K-M Semi-arid Cold Mild 3.0 47,039 SH-C-VW Sub-humid Cool Very warm 0.0 344 SH-C-W Sub-humid Cool Warm 0.5 8,380 SH-K-W Sub-humid Cold Warm 0.8 12,248 SH-K-M Sub-humid Cold Mild 1.0 15,529 SH-K-C Sub-humid Cold Cool 0.0 33 H-C-W Humid Cool Warm 0.3 4,682 H-K-W Humid Cold Warm 0.0 395 H-K-M Humid Cold Mild 0.0 419 H-K-C Humid Cold Cool 0.0 53 PH-C-W Per-humid Cool Warm 0.5 8502 PH-K-W Per-humid Cold Warm 0.0 48 PH-K-M Per-humid Cold Mild 0.0 8 PH-K-C Per-humid Cold Cool 0.0 19 COMPARISON WITH EXISTING CLIMATIC MAPS Existing climatic maps either deal with a single climatic parameter, such as precipitation (Fig. 2), or approach climate from a special perspective, such as soil climate (Fig. 3). Fig. 4 shows an alternative climatic map of Iran with 8 climate types, provided by the Rangeland and Forestry Organization, Tehran). Criteria and thresholds used for developing the map are unknown. The new ACZ map, with 28 climatic subdivisions, fills an important data gap as it integrates moisture supply and demand and thermal regimes into a single map.