ECOLOGIA BALKANICA 2015, Vol. 7, Issue 1 June 2015 pp. 29-38 Impacts of Climate Change on Droughts in Gilan Province, Iran Ladan Kazemi Rad1*, Hosein Mohammadi2, Vahid Teyfoori3 1 - Academic center for education, culture & research, Rasht, IRAN 2 - Department of Geography, Tehran University, IRAN 3 - Statistics Governor of Gilan, IRAN *Corresponding author: [email protected] Abstract. Drought as a complex natural hazard is best characterized by multiple climatological and hydrological parameters and its assessment is important for planning and managing water resources. So understanding the history of drought in an area is essential like investigating the effects of drought. In this study at first climate parameters affecting the drought have downscaled by LARS-WG stochastic weather generator over Gilan province in Iran. After choosing a suitable model, the outputs were used for assessing the drought situation in the period of 2011-2030. Assessing the drought was done by TOPSIS method during 2 periods (present and future). After validation of the method, zoning the drought was performed by IDW method in GIS. Results showed that the expanse of situations with lower drought index will increase. Also we will expect more droughts in these regions for the future. Climate change, Drought, GCM model, LARS-WG, TOPSIS method, Gilan province. Keywords: Introduction scenarios of future climate (WILKS, 1992; Observed changes in the climate due to MEARNS et al., 1997; SEMENOV & BARROW, increasing greenhouse-gas concentrations 1997). Two important reasons for using have made it essential to investigate these LARS-WG model include the provision of a changes. The application of General means of simulating synthetic weather time- Circulation Model (GCM) results is the most series with certain statistical properties current method in use in climate change which are long enough to be used in an studies. Although GCMs are imperfect and assessment of risk in hydrological or uncertain, they are a key to understanding agricultural applications and providing the changes in climate. Output from GCMs means of extending the simulation of requires application of various downscaling weather time-series to unobserved locations. techniques (BARROW et al., 1996; BARDOSSY, It has been used in various studies, 1997; WILBY et al., 1998; MEARNS et al., 1999; including the assessment of the impacts of MURPHY, 1999; SALON et al., 2009). One of climate change (BARROW & SEMENOV, 1995; the downscaling techniques to create daily SEMENOV & BARROW, 1997; WEISS et al., 2003; site-specific climate scenarios makes use of a LAWLESS & SEMENOV, 2005; KHAN et al., 2006; stochastic weather generator (WILKS, 1992; SCIBEK & ALLEN, 2006; SEMENOV, 2007; BARROW & SEMENOV, 1995; WILKS & WILBY, SEMENOV & DOBLAS, 2007; DUBROVSKY, 1999; SEMENOV, 2007). Recently, weather 1996). generators have been used in climate Owing to the rise in water demand and change studies to produce daily site-specific looming climate change, recent years have © Ecologia Balkanica Union of Scientists in Bulgaria – Plovdiv http://eb.bio.uni-plovdiv.bg University of Plovdiv Publishing House Impacts of Climate Change on Droughts in Gilan Province, Iran witnessed much focus on global drought better choices in various situations. This is scenarios. Droughts are recognized as an the motivation of our study. environmental disaster and occur in In this study at first changes in the virtually all climatic zones. They are mostly climate variables are studied in Gilan, a related to the reduction in the amount of Province in north of Iran. The output from precipitation received over an extended two GCM models was compared with a period of time, such as a season or a year stochastic weather generator, LARS-WG (WILHITE, 1992). They impact both surface (Long Ashton Research Station Weather and groundwater resources and can lead to Generator) and suitable model used to reduced water supply, deteriorated water produce a climate change scenario. Then quality, crop failure, reduced range TOPSIS method is used for determining and productivity, diminished power generation, ranking of drought in the study area for 2 disturbed riparian habitats, and suspended periods (present and future). recreation activities, as well as affect a host of economic and social activities (RIEBSAME Materials and Methods et al., 1991). They also affect water quality, as The study area is one of the 31 moderate climate fluctuations alter provinces of Iran. There is in the north of hydrologic regimes that have substantial Iran and located in the South of Caspian Sea effects on the lake chemistry (WEBSTER et al., and has about 14000 kilometers in extent. 1996). Location of longitude is between 48 degrees Generally, drought is a phenomenon 53 minutes and 50 degrees 34 minutes and which occurs in every area or country, with latitude is between 36 degrees 34 minutes either arid or humid climate and in our and 38 degrees 27 minutes, as shown in country. In fact, Iran’s natural conditions Fig.1. This area was selected to enable the and its geographical location are so that we researchers to collect data from a variety of have always witnessed droughts and it can climatic zones in the north of Iran (near the be said that some of the regions are often Caspian Sea). It has the best type of weather faced with the phenomenon (KARDAVANI, in Iran with a moderate and humid climate 2001). However there are definitions and that is known as the moderate Caspian models for measuring the qualitative and climate. The effective factors behind such a quantitative of this phenomenon but there is climate include the Alborz mountain range, no real comprehensive model to have all direction of the mountains, the height of the climatic, hydrological, agricultural, social area, and the Caspian Sea, vegetation and so on conditions and be responsive to surface, local winds, as well as the altitude the needs. and weather fronts. As a result of the above MADM is a practical tool for selecting factors, three different climates exist in the and ranking of a number of alternatives, its region: applications are numerous. In recent years, 1. Plain moderate climate with an TOPSIS has been successfully applied to the average annual rainfall amounts to 1200 or areas of human resources management 1300 mm, decreasing to the east. (CHEN & TZENG, 2004), transportation 2. Mountainous climate which covers (JANIC, 2003), product design (KWONG & the high mountains and northern parts of TAM, 2002), manufacturing (MILANI et al., the Alborz range. In the heights, the weather 2005), water management (SRDJEVIC et al., is cold mountainous and most of the 2004), quality control (YANG & CHOU, 2005), precipitation is in the form of snow. and location analysis (YOON & HWANG, 3. Semi-arid climate with the average 1985). In addition, the concept of TOPSIS annual rainfall stands at 500 mm and the has also been connected to multi-objective average annual temperature is 18.2°C. decision making (LAI, 1994) and group Firstly, the performance of the LARS- decision making (SHIH et al., 2001). The high WG stochastic weather generator model was flexibility of this concept is able to statistically evaluated by comparing the accommodate further extension to make synthesized data with climatology period at 30 Ladan Kazemi Rad, Hosein Mohammadi, Vahid Teyfoori 8 selected synoptic stations, based on 2 temperature in ºC, Days with precipitation GCMs models (MPEH5, HADCM3) and 2 more than 0.1 mm (Number of wet days), scenarios (A2, B1). Days with precipitation less than 0.1 mm (Number of dry days), Days with maximum temperature more than 30ºC (Number of hot days), Days with minimum temperature equal or less than 0ºC (Number of frost days) that are influencing on drought are used. Missing data are estimated by regression method and homogeneity of data is determined by Run-Test method. By using TOPSIS method and Excel software, droughts are identified and ranked in the study area for 2 periods (present and future). Then output data were compared with SIAP method. Finally, by using the interpolation method (IDW) in ArcGIS 9.3 software, zoning drought of study area is classified for 2 periods (present and future). Steps of operations can be expressed as followed: 1. Obtain performance data for 19 Fig. 1. Study area location alternatives (Number of statistical years) over 7 criteria (Climatic Parameters). Raw Name, latitude and longitude measurements are usually standardized, coordinates, as well as the elevation of the X = (X )n× m (1) synoptic stations are shown in Table 1. After ij assessing LARS-WG ability in each station, 2. Develop a set of importance weights it was performed for all 4 states (2 GCMs wj, for each of the criteria. models based on 2 scenarios). Then the m (2) = = results were compared and the best model ∑ w j 1, j 1,2, ,m. was chosen for predicting climatic j=1 parameters in the study area. Doing this section has 4 steps: Step 1: Determining distribution of each Table 1. Synoptic stations utilized in the climatic parameter. study (3) rij pij = m :∀i, j Latitude Longitude Elevation Stations ∑ r (°N) (°E) (m) i=1 Anzali 37° 29' 49° 27' -23.6 Ardebil 38° 15' 48° 17' 1332 Step 2: Calculating Anthropy for Astara 38° 22' 48° 51' -21.1 expressing amount of uncertainty in this Ghazvin 36° 15' 50° 3' 1279.2 distribution. Manjil 36° 44' 49° 25' 338.3 (4) Ramsar 36° 54' 50° 4' -20 Rasht 37° 19' 49° 37' -8.6 m 1 E = −k [p .Ln(p )]: ∀ k = Zanjan 36° 41' 48° 29' 1663 j ∑ ij ij j i=1 Ln m TOPSIS method is used for determining Step 3: Calculating uncertainty for each and ranking of drought in the study area. 7 climatic parameter. Climatic Parameters consisting Precipitation = − ∀ (5) in mm, Maximum and Minimum d 1 E j : j 31 Impacts of Climate Change on Droughts in Gilan Province, Iran Step 4: Calculating weight of climatic 8.