ARMENIA: LUSADZOR VILLAGE
CLIMATE CHANGE IMPACT ASSESSMENT
June 2009 Yerevan, Armenia Project Title: Climate Change Impact Assessment, 00049248
Project Coordinator: Diana Harutyunyan
Implementing Agency: UNDP Armenia
Executing Agency: Ministry of Nature Protection of the RA
This assessment was undertaken by UNDP Armenia and performed by Mr. Vahagn Tonoyan, National Expert on Climate Change Assessment.
The team worked in close co-operation with Tavush marz and Lusadzor village administrations whose support is highly appreciated.
The team acknowledges important contribution of UNDP Second National Communication Expert Team and Community Development Project Team to the report elaboration.
The author also highly appreciates valuable contribution of “ArmStateHydoMet” SNCO in data compilation.
Special thanks go to Keti Chachibaia, Regional Technical Advisor on Climate Change Adaptation and Capacity Development at UNDP Bratislava Regional Centre for her support and guidance.
Climate Change Information Center Ministry of Nature Protection
Address Republic Square, Government building 3, Yerevan, 0010 Phone (37410) 583932, 583920 Fax (37410) 583933 E-mail [email protected] Web Page http://www.nature-ic.amhttp://www.nature-ic.am
TABLE OF CONTENTS
1. INTRODUCTION ...... 2 2. BACKGROUND INFORMATION ...... 3 2.1 Location ...... 3 2.2 Socio-Economic Data ...... 3 2.3 Agriculture ...... 3 2.4 Non-Agricultural Activities ...... 4 2.5 Water...... 4 2.6 Health Care ...... 5 2.7 Transport and Communication...... 5 2.8 Utilities...... 5 2.9 Local Climate and Topography...... 5 3. METHODOLOGY FOR DATA COLLECTION...... 6 4. MAIN FINDINGS OF THE INTERVIEWS...... 7 5. ANALYSIS OF AIR TEMPERATURE AND PRECIPITATION DATA ...... 8 5.1 Analysis of Air Temperature and Precipitation Data...... 8 5.2 Air Temperature...... 8 5.3 Atmospheric Precipitation...... 10 5.4 Extreme Weather Events...... 12 5.5 Forecasted Climate Change ...... 14 6. SCREENING OF COMMUNITY DEVELOPMENT PROJECTS ON THEIR CLIMATE CHANGE SENSITIVITY...... 15 7. IDENTIFICATION AND ASSESSMENT OF ADAPTATION MEASURES ...... 17 7.1 Proposed Adaptation Measures...... 17 7.2 Economic Assessment of Adaptation Measures ...... 19 7.2.1 Economic Assessment of Adaptation Measures ...... 19 7.2.2 Cost Assessment of the Proposed Adaptation Measures ...... 20 7.3 Status of Implementation of the Proposed Adaptation Measures...... 24 ANNEXES...... 27 Annex 1. Technical Report based on findings during the mission to Yerevan 11-14 September 2007...... 27 Annex 2. Notes to the project TOR Lusadzor Community: Integrated Development Plan...... 36 Annex 3. Assessment of the pilot climate change impact assessment of the Lusadzor community project in Armenia ...... 41 Annex 4. Location of Lusadzor Community ...... 47 Annex 5. Main Climate Features of Lusadzor ...... 48 Annex 6. Questionnaire for Field Survey ...... 49 Annex 7. Meteorological Stations in Tavush Marz ...... 53 Annex 8. Average Monthly Temperature, Ijevan ...... 54 Annex 9. Average Monthly Precipitation, Ijevan ...... 56 Annex 10. Average Monthly Temperature, Berd ...... 58 Annex 11. Average Monthly Precipitation, Berd ...... 60 Annex 12. Comparison of the Average Monthly Temperatures, Ijevan...... 62 Annex 13. Comparison of Average Precipitation, Ijevan ...... 64 Annex 14. Comparison of Average Air Temperature, Berd...... 66 Annex 15. Comparison of Average Precipitation, Berd ...... 68 Annex 16. Data on Extremes in Average Monthly Temperature and Precipitation, Ijevan ...... 70 Annex 17. Bibliography...... 71
1 “Climate Change Impact Assessment” UNDP/00049248 1. INTRODUCTION
Climate change is one of the greatest challenges facing the world’s environment, society and economy today. Its impacts can already be seen across the globe, and Armenia will not be immune. Recognizing that UNDP’s work in the country may be affected by both current and future climate variability and extremes, UNDP Armenia with support of UNDP Bratislava Regional Centre initiated climate proofing of the UNDP’s poverty reduction programme in Armenia.
Climate proofing process started with the mission of the UNDP/GEF Regional Technical Advisor (RTA) on Adaptation in September 20071. The RTA carried out initial screening of the UNDP country portfolio and identified a number of projects particularly susceptible to climate risks, which would benefit from climate mainstreaming. The Community Development Programme (CDP), one of the UNDP flagship programmes in Armenia that has mobilized governmental commitment and donor interest, has been identified as a priority to introduce climate risk management system. The core objective of the programme is to improve local livelihoods and minimize incidents of poverty. The programme targets communities and clusters of communities in bordering regions (Marz) and villages, that are at high risks of social and cross-border conflicts. The main rationale for selecting CDP is that the programme supports local development in climate sensitive areas. Situation analysis has recognized extreme weather events and climate disasters, such as floods and droughts, as important risks interrupting local development schemes. However, no targeted efforts have been made so far to address these risks in local planning and pilot activities. Two additional reasons for selecting the programme were that it had a long timeframe, significant budget and some of the pilot activities were still at the design phase.
Within the CDP, the “Integrated Development Plan for Lusadzor Community” project (hereafter Project), which started in 2007, has been selected as a pilot for integrating climate risks management. Initial scoping exercise identified Tavoush Marz (where Lusadzor is located) as one of the disaster prone regions of Armenia2. In particular, occurrences of floods, hails, frosts and mudflows constitute main climatic disasters. Low agricultural productivity, arable land abandonment (due to poor irrigation and access to arable lands), and poor rural infrastructure (housing issues, roads and bridge to connect to the area with arable land) are among the top priority issues for the local community development. High climatic variability and future changes in mean parameters may exacerbate the current development challenges and even undermine planned efforts to address them. Therefore, integration of climate risk management into the community development efforts is highly relevant.
This report is a detailed account of an in-depth climate risk assessment of the Lusadzor Project, which underpinned climate proofing exercise for the Community Development Programme in Armenia. Risk assessment aimed to i) analyze current and projected climate change impacts on Lusadzor community, ii) identify those impacts that could affect the Project activities in Lusadzor and iii) select a number of priority adaptation options to be implemented by UNDP, local authorities and donors.
The in-depth climate risk assessment was conducted in three interrelated steps, which often ran in parallel: 1. Gathering and analysis of climate-related data on Lusadzor (covered in Part 4 and 5 of this report);
1 The mission report and screening notes can be found in Annex 1 2 Screening notes to the Luzadsor climate risk assessment can be found in Annex 2
2 “Climate Change Impact Assessment” UNDP/00049248 2. Identification of climate risk-prone activities in the “Integrated Development Plan for Lusadzor Community” project (covered in Part 6); 3. Identification and costing of adaptation measures (covered in Part 7).
Following the finalization of the climate risk assessment exercise, the whole process went through an external quality control performed by an independent expert. The assessment report is presented in Annex 3.
2. BACKGROUND INFORMATION
2.1 Location
Lusadzor village is a border village in Ijevan district of Tavush marz (Annex 4); the distance from the border with Azerbaijan is 9 km. The village is located 700 m above the sea level. The distance from the village to marz centre Ijevan is 7 km, to the capital city Yerevan - 145 km, and to Yerevan- Tbilisi main republican road – 1 km.
2.2 Socio-Economic Data
There are 197 inhabited households in Lusadzor. The number of resident population is 696 and actual population as of 2007 is 724, including 359 male and 365 female. The education level of the community population is high. Among the community population above 16 years old, 25% have higher or secondary vocational degrees.
The administrative territory of Lusadzor is 626 ha, of which 462 ha (73.8%) is agricultural land, 82 ha are forest and water reserves, 43 ha are settlements, including gardens, and 39 ha are used for other purposes.
Agricultural activities are the main source of employment and income for the village population. The number of employed in this sector is 243, or 33,5% of total local population. Eighty-two people work in public (marz or village) and private sectors; nine are currently on military services for 2 years; 11 are students. Forty-five households, or 22.8% of the households actually living in the village, receive family allowances.
2.3 Agriculture
Out of 462 hectares of agricultural land, 158 hectares (34%) are arable land, 187 hectares comprise pastures, and 117 ha are not used. In addition to this, there are also 26 ha of homestead land, which is also being used for agricultural purposes. As a result of no access to irrigation water, absence of chemical pesticides and fertilizers, the community arable land is not cultivated and part of it has turned into meadows and pastures.
In order to increase the effectiveness of land use, under the framework of program on “Natural resources management and poverty reduction” administered by RoA Ministry of Nature Protection, five hectares of fruit orchards with 2,500 trees were planted on the non-privatised community land in 2007.
3 “Climate Change Impact Assessment” UNDP/00049248 The agriculture in Lusadzor is directed toward internal consumption. Main items of agricultural production include potato, grain, corn, vegetables (mainly tomatoes), fruits (mainly blood orange and fig), milk, wool, eggs, honey (see Table 1). In recent years, tobacco production has sharply dropped because of decrease in the purchasing prices. Processed grain and perennial plants from non-irrigated lands are dominantly used by farmers for their own needs. Often, grain is used for feeding livestock due to its poor quality.
Table 1. Agricultural production in Lusadzor (2006) Items of production Amount potato 110 tons grain 147.5 tons tobacco 1 ton meat 30 tons milk 230 tons wool 300 kg eggs 180,000 honey 300 kg
There are 292 heads of cattle, of which, 155 cows; 177 sheep and goats; 60 pigs; 21 donkeys and mules; 1,190 heads of poultry, as well as 18 bee colonies in the community.
There is a serious problem with sale of the agricultural goods produced with great hardship. Villagers sell the products at prices lower than the market prices to resellers, who make purchases in the community rather irregularly, or transport the goods to the closest market in Ijevan on their expense.
2.4 Non-Agricultural Activities
On the territory of the community, three commercial counters operate with three employees. A stone-working workshop also operates in the village, which has three employees. There are perferrite and dolomite mines in Lousadzor.
2.5 Water
Potable water comes from natural water sources. The community has no internal network of potable water. Four potable water pipelines, two of them – individual, serve the needs of the community population. Two drinking fountains (one is situated in the village centre and the other – in the upper village district) are connected to one community water pipeline. Around 80% of households use these two drinking fountains. The water spring that feeds the fountain's water pipe is in poor condition, and there are significant water losses. Part of the population receives and drinks "technical" water, which is unusable. There are two water reservoirs in the community that do not operate.
Irrigation in the community is carried out both through gravity flow and through the Aknaghbyur- Lousadzor pump station, which is managed by "Ijevan" Water Users' Association (WUA). The water system with gravity flow is owned by the community and is not managed by the "Ijevan" WUA. According to the data of the State Committee on Water Systems under the Ministry of Territorial Administration, the community has 123 hectares of irrigable land. According to information received from various sources (village inhabitants, community municipality, RoA State Water Committee, "Ijevan" WUA), in 2006, 72 hectares were actually irrigated, of which, 20 hectares were irrigated with water from the Aghstev River through the pump station, and 52
4 “Climate Change Impact Assessment” UNDP/00049248 hectares – partly or totally through gravity flow. The villagers consider irrigation through the pump station as not accessible to them because of its high price.
The irrigation problem in the community is envisaged to be resolved in 2009 in the framework of the "Millennium Challenges" programme by constructing Getahovit-Lousadzor irrigation water pipeline that will operate through gravity flow. As a result, 132 hectares of arable land will be irrigated through gravity flow, and the agricultural yield will increase several times.
2.6 Health Care
Healthcare services are provided by the village medical unit, which is located in the municipality's administrative building and was partly renovated by NOVA organisation. One medical specialist works on prevention and early diagnosis of diseases in the community. Resources for primary healthcare are insufficient.
2.7 Transport and Communication
Out of 6 kilometres of intra-community roads, only 1.5 km is asphalted, and the rest needs renovation. The renovation of roads that connect the village with fields, as well as the bridge pass connecting arable land with Mtnadzor, Krotu, Sarhat and Ghourdar almost impassable gorges is an issue in the community. This has already started to be implemented by the RoA Ministry of Nature Protection (pipeline passages with metallic-concrete pipes of different diameters, construction of anti-erosive, anti-damage supporting walls, levelling of field roads and covering with road-metal).
The transportation in the community is satisfactory; in particular, Lousadzor-Ijevan bus operates four times per day. The price for a one-way ticket is 100 dram. There is mobile telephone communication in the community.
There are only 12 subscribers in the village who are connected to the obsolete and worn-out analogue telephone system with a low connection quality.
The municipality is not subscribed to any republican newspaper. The community receives a local newspaper, "Farmer", free of charge. Lousadzor population can watch seven TV channels, including one local (Ijevan-based).
2.8 Utilities
Natural gas is supplied to 30 households in the community. The main 1.5 km-long gas pipeline was completed in summer of 2006. In the third quarter of this year a natural gas network (350 m) will be constructed in the school, which will allow additional 20 households to use natural gas. For the total gasification of the community it is necessary to construct 3 km of new gas pipelines3.
Garbage collection in the community is not regulated in any way. The street lighting system does not operate.
2.9 Local Climate and Topography
According to Armenian State Hydro-meteorological Service the average annual temperature in Lusadzor village is 110C. On average, there are 150 sunny days. The average annual precipitation
3 The community has a stock of pipes of 2 kilometers long.
5 “Climate Change Impact Assessment” UNDP/00049248 amounts to approximately 600 mm. Annex 5 provides information on the main climate features of Lusadzor.
Soil in Lusadzor community is of mountain-steppe category. Two types of terrain are found in the administrative territory of the community: low and middle mountain steppe; and low mountain dry steppe. Vegetation type belongs to wood land vegetation type.
3. METHODOLOGY FOR DATA COLLECTION
Based on consultations with the Second National Communication experts and the data currently available to the project team, the initial scoping exercise concluded that Lusadzor is located in one of the disaster prone regions of Armenia. However, existing general information about occurrences of extreme weather events only allowed for a general decision about importance of the climate risk management to the successful implementation of the project. Thus, the initial scoping exercise recommended that in addition to the analysis of the Hydromet data, the bottom-up V&A assessment is undertaken in order to come up with a meaningful adaptation strategy for the project.
Consequently, two approaches have been used to identify past and current climate change impacts on Lusadzor community: i) interviews with local community and representatives of Department of Agriculture of Tavush Marzpetaran:
A questionnaire (see Annex 6) was developed for interviewing local residents, including the rural community administration. The questionnaire contained three sets of questions. The first set of questions included general information, such as landholding size, agricultural practices, and main sources of income. The second set of questions related to climatic hazards occurred in the community in the recent years, such as floods, mudflows, landslides, droughts, hail, wind and etc. Questions on residents’ perception of the frequency, intensity of climatic hazards, types of losses and magnitude were included for each type of climatic hazard. The last part of the questionnaire related to coping with climate extremes. Particularly, the residents were asked about the accessibility and use of climate information, types of adjustments made to cope with climate risks, including indigenous knowledge, obstacles faced while coping with climate risks, and others.
Two field trips were organized to Lusadzor community in order to conduct survey of the local residents in line with the above-mentioned questionnaire items. In total, 12 families were interviewed, including the rural community leader.
In order to find out economic losses due to climatic hazards, meetings were held with the representatives of Department of Agriculture of Tavush Marzpetaran and data on official economic losses was collected. ii) analysis of meteorological data from two meteorological posts nearby the Lusadzor community:
Armenian State Hydro meteorological Service does not have any meteorological stations or posts in Lusadzor community (Annex 7). In order to analyze the air temperature and atmospheric precipitation, data from two nearby meteorological posts (Ijevan, 8.2 km south of Lusadzor, and Berd, 27 km east of Lusadzor) were taken as a basis for trend analysis. Given the similarity of climatic conditions in the two meteorological posts as compared to Lusadzor community, some conclusions can be drawn based on air temperature and atmospheric precipitation data retrieved
6 “Climate Change Impact Assessment” UNDP/00049248 from Ijevan and Berd meteorological posts. It should be noted, however, that the validity of analysis is highly contingent upon the accuracy of air temperature and precipitation data recorded by the Armenian State Hydrometeorological Service.
Temperature has been measured at the Ijevan meteorological post since 1913. However, since the period 1961-1990 is considered as a baseline scenario, in this analysis the period of temperature records was divided into two parts – 1961-1990 and 1991-2006. The precipitation (in mm) records of the Ijevan post has been studied for the period of 1961 till 2006. Temperature has been measured at the Berd meteorological post since 1934; in 2003 the meteorological station was closed. Therefore, for the purpose of the analysis, the temperature and precipitation records were divided into two periods – 1961-1990 and 1991-2002. Average monthly temperature and precipitation data (since 1935) recorded at these two observation points are presented in Annexes 8-11.
The analysis of the weather extremes was conducted based on data from Ijevan meteorological station only because Berd meteorological station is not operational since 2003.
4. MAIN FINDINGS OF THE INTERVIEWS
The main findings of the interviews are presented below:
The frequency and intensity of climatic hazards has increased
75% of the 12 interviewed families mentioned that the frequency and intensity of climatic hazards has increased in the period of 2001-2007. This particularly relates to flooding, mudflows, hail, strong winds and frost. Also, according to local authorities and population, due to intensification of weather extremes, less water is available.
There are tangible economic losses due to climatic hazards
None of the climatic hazards has caused human losses. However, significant damage was caused to infrastructure (destruction of roofs due to storms, washing out of land area due to flooding, etc.). The residents claim significant economic losses due to climatic hazards, however, 67% of the interviewed found it difficult to assess the magnitude of economic losses on their land area due to climatic hazards, while others presented quite scattered figures.
According to the representatives of Department of Agriculture of Tavush Marzpetaran, in the period 2004-2007, significant economic losses occurred in Lusadzor community due to climatic hazards. For each case special inter-agency commission has assessed the monetary value of economic loss. Total official economic losses due to climatic hazards in 2004-2007 comprise over 24 million AMD or approximately 57,500 USD. Detailed breakdown of economic losses due to climatic hazards in Lusadzor community is provided in the table below.
Lack of information is one of the main barriers to coping with climate risks
All of the residents stated that climate information is not accessible at all. They do not receive any climate information or forecast beforehand, and thus are not able to use it. All of the residents stated that their farm is sensitive to climate impacts, and the only adjustment they make to cope is to cover tree roots with leafs and ashes to protect trees from frost. Most of the respondents do not have clear ideas on adaptation measures, and consider lack of information as one of the main barriers to coping with climate risks.
7 “Climate Change Impact Assessment” UNDP/00049248 Table 2. Detailed breakdown of economic losses due to climatic hazards in Lusadzor community Year Date Climatic Description Economic Harvest Total, Total, by Hazard loss in loss, % by year in years in AMD AMD USD4 June 1 Hail Damage to wheat 1,575,000 15 1,575,000 2,950 2004 June 8 Hail Damage to grapes 189,000 15 189,000 350 March 9 Storm Damage to April 28- Early Potatoes 1,450,000 10 29 spring Corn 12,960,000 90 2006 22,300,000 53,570 freezing Beans 1,750,000 70 Vegetables 400,000 10 May 19 Drought Fruits 5,740,000 70 2007 May Flooding Washing out 0.10 199,000 1005 199,000 590 ha of land Total damage for the period 2004-2007 in AMD 24,263,000 57,460
5. ANALYSIS OF AIR TEMPERATURE AND PRECIPITATION DATA
5.1 Analysis of Air Temperature and Precipitation Data
Data from both meteorological stations in Ijevan and Berd demonstrate increase in average annual temperature and decrease in annual average atmospheric precipitation in the last 10-15 years. The analysis shows that the air temperature increase has been especially noticeable during summer months.
This is in line with model stimulations for Armenia6, which demonstrate that for the overall territory of Armenia air temperature increases and atmospheric precipitation decreases. It should be noted that 2000 and 2006 were particularly dry years. Moreover, the summer of 2006 was the hottest in Armenia during the period 1929-2007.
Observations at the Ijevan meteorological station also support comments of the local community on intensification of the weather extremes in last years.
Decrease in water availability, observed by local population due to intensification of weather extremes, cannot be easily justified by actual data due to the absence of hydrological observation points on Lusadzor River. Some initial conclusions might be made since Lusadzor is a tributary to Aghstev River. According to draft report on “Assessment of Vulnerability of Armenia’s Water Resources due to Climate Change” prepared within the Second National Communication [2] in Aghstev River the average flow in 1991-2006 has declined by 3.5% as compared to 1961-1990 average.
5.2 Air Temperature
Data from Ijevan meteorological post showed an increase of 0.620C, or from 11.170C to 11.790C, between average annual temperature in 1961-1990 and average annual temperature in 1991-2006.
4) Corresponding annual exchange rates were the following: For 2004 - 1 USD = 533.45, For 2006 -1 USD = 416.29 and For 2007 - 1 USD = 338.75 5) 100 percent loss here refers to 0.1 ha of washed land 6 Model Simulations of Climate Change over Armenia Region, 2008
8 “Climate Change Impact Assessment” UNDP/00049248 For the period of 1991-2006 an increase of average monthly air temperature is observed in the months January-October, as compared to the period 1961-1990 (Figure 1). Moreover, noticeable air temperature increase is registered for the months March, August and October, correspondingly 1.010C, 1.710C, and 1.190C. On the other hand, slight air temperature decrease is observed for the months November and December (-0.310C and -0.160C correspondingly). The analysis shows that more significant air temperature increase occurs during summer months (+3,140C as compared to 1961-1990), whereas in winter - only slight increase is observed (+0,310C as compared to 1961- 1990).
Average monthly T for 1960-1990 Average monthly T for 1991-2006
25.00
20.00
15.00
10.00 Temperature 5.00
0.00 123456789101112 Months
Figure 1. Average monthly temperature for the periods of 1961-1990 and 1991-2006, Ijevan meteorological station Comparison of average air temperature for the periods 1961-1990 and 1991-2006 by individual months recorded at the Ijevan meteorological station is presented in Annex 12.
The absolute maximum average air temperature, 260C, was recorded in August 2006. Maximum average monthly air temperatures have been recorded in March 2001 (9.20C), April 1998 (14.30C), June 2006 (22.50C), July 2000 (25.30C) and October 1997 (15.00C). Thus, the maximum average monthly temperatures for all spring and summer months (except May) were recorded in the last 10- 15 years. The maximum average monthly air temperatures for other months were recorded within the period 1961-1990.
As for seasonal distribution of air temperature measured at Ijevan meteorological station, average winter, spring, summer and fall air temperatures have been analyzed for the periods 1961-1990 and 1991-2006. The analysis shows that the highest air temperature increase is observed in summer +10C, whereas the lowest air temperature increase is observed in spring +0,410C. The table and figure below provide brief analysis of seasonal changes in air temperature recorded in Ijevan meteorological station.
Table 3. Seasonal changes in air temperature in Ijevan meteorological station Winter Spring Summer Fall 1961-1990 1,97 10,18 20,52 12,01 1991-2006 2,55 10,59 21,53 12,51 Difference +0,58 +0,41 +1,00 +0,49
An increase in annual average air temperature of 0.770C, from 11.170C to 11.940C, is also observed at the Berd meteorological post for the period of 1991-2002 as compared to 1961-1990.
9 “Climate Change Impact Assessment” UNDP/00049248
For the period of 1991-2002 an increase in average monthly air temperature is observed in all months as compared to the period 1961-1990 (Figure 2). Air temperature increase in April, August and October is correspondingly 1.190C, 1.050C, and 1.920C. In Berd the most significant air temperature increase also occurred during summer months (+2,170C as compared to 1961-1990).
Comparison of average air temperature for the periods 1961-1990 and 1991-2002 by individual months recorded at the Berd meteorological station is presented in Annex 14.
Average monthly T for 1960-1990 Average monthly T for 1991-2002
25.00
20.00
15.00
10.00 Temperature 5.00
0.00 123456789101112 Months
Figure 2. Average monthly temperature for the periods of 1961-1990 and 1991-2006, Berd meteorological station
As for seasonal distribution of air temperature measured at Berd meteorological station, average winter, spring, summer and fall air temperatures have been analyzed for the periods 1961-1990 and 1991-2002. The analysis shows that the highest air temperature increase is observed in spring +1.350C, whereas the lowest air temperature increase is observed in winter +0,440C. The table and figure below provide brief analysis of seasonal changes in air temperature recorded in Berd meteorological station.
Table 4. Seasonal changes in air temperature in Berd meteorological station Winter Spring Summer Fall 1961-1990 1,34 10,23955 21,13113 11,99313 1991-2002 1,78 11,58737 21,84733 13,20314 Difference +0,44 +1,35 +0,72 +1,21
5.3 Atmospheric Precipitation
Observations at the Ijevan station show a decrease of 6 mm in annual average atmospheric precipitation in the period of 1991-2006 as compared to 1961-1990. The average annual precipitation for the period 1961-1990 was 587 mm, whereas the average annual precipitation for the period 1991-2006 was 581 mm.
For the period of 1991-2006 a decrease of average monthly precipitation is observed in the months of January, February, May and October as compared to the period 1961-1990. On the other hand, increase of average monthly precipitation is observed in all other months (Figure 3).
10 “Climate Change Impact Assessment” UNDP/00049248 Comparison of average precipitation for the periods 1961-1990 and 1991-2006 by individual months recorded at Ijevan meteorological station is presented in Annex 13.
The absolute maximum average monthly precipitation within the entire observation period (1961- 2006) occurred in August 2002, when the quantity of precipitation was 179.1 mm.
Average monthly P for 1960-1990 Average monthly P for 1991-2006
120.00 100.00 80.00
60.00 40.00
Precipitation, mm Precipitation, 20.00 0.00 123456789101112 Months
Figure 3. Average monthly precipitation for the periods of 1961-1990 and 1991-2006, Ijevan station
As for seasonal distribution of precipitation, a comparison has been made between the total precipitation in winter, spring, summer and fall seasons for the periods of 1961-1990 and 1991- 2006 in Ijevan meteorological station. The analysis shows that in spring and summer precipitation has increased, whereas in fall and winter precipitation has decreased. The table and figure below provide brief summary of comparison of seasonal distribution of precipitation for the above- mentioned periods.
Table 5. Seasonal changes in precipitation in Ijevan meteorological station Winter Spring Summer Fall 1961-1990 80 202 187 124 1991-2006 65 207 197 118 Difference -15 5 10 -6
At the Berd station, a sharp decrease of 169 mm in annual average atmospheric precipitation is observed for the period of 1991-2002 as compared to 1961-1990. For the period 1961-1990 the average annual precipitation has been 464 mm, whereas for the period 1991-2006 it has been 295 mm.
For the period of 1991-2002 a decrease of average monthly precipitation is observed in all months as compared to the period 1961-1990. Particularly for the months June-October (1991-2002), average monthly precipitation has decreased by 108 mm (49%) as compared to the same period of 1961-1990 (Figure 4). However, such a huge difference is likely to be due to the fact that several measurements for the period of 1991-2002 were not done.
11 “Climate Change Impact Assessment” UNDP/00049248 Average Monthly P for 1960-1990 Average Monthly P for 1991-2002
80.00 70.00 60.00 50.00 40.00 30.00 20.00 Precipitation, mm Precipitation, 10.00 0.00 123456789101112 Months
Figure 4. Average monthly precipitation for the periods of 1961-1990 and 1991-2006, Berd meteorological station
Comparison of average atmospheric precipitation for the periods 1961-1990 and 1991-2002 by individual months recorded at the Berd meteorological station is presented in Annex 15.
As for seasonal distribution of precipitation, a comparison has been made between the total precipitation in winter, spring, summer and fall seasons for the periods of 1961-1990 and 1991- 2002 in Berd meteorological station. The analysis shows sharp decline in precipitation in spring, summer and fall of 1991-2002 as compared to the corresponding seasons of the period of 1961- 1990. The table and figure below provide brief summary of comparison of seasonal distribution of precipitation for the above-mentioned periods.
Table 6. Seasonal changes in precipitation in Berd meteorological station Winter Spring Summer Fall 1961-1990 65 158 137 104 1991-2002 57 126 65 47 Difference -8 -32 -72 -57
However, it should be noted that it is difficult to make justified conclusions regarding Berd meteorological stations due to incomplete observations in the period of 1991-2002.
5.4 Extreme Weather Events
Meteorological data from Ijevan station suggests intensification of the weather extremes in Lusadzor community in 1991-2006 comparing to 1961-1990.
As for the air temperature data from Ijevan meteorological station, the analysis shows that for the period of 1991-2006 in 21 months the average monthly air temperature has been over 50% more than the long-term average monthly air temperature for the respective month of the period of 1961- 1990. On the other hand, for the period of 1991-2006 in 11 months the average monthly air temperature has been over 50% less than the corresponding data for 1961-1990. It is also worth to mention that all these variations occurred in the months December-March.
12 “Climate Change Impact Assessment” UNDP/00049248 Thus, for the period of 1991-2006 in almost 17% of cases (months) average monthly air temperature varies from the average monthly air temperature for the period of 1961-1990 by more than 50%. It is also worth to mention that all these variations occurred in the months December- March. Figure 5 presents a comparison of the average monthly air temperatures for the period 1961- 1991 and corresponding maximum and minimum average monthly air temperatures for the period 1991-2006 recorded in Ijevan meteorological station.
30.0
25.0 Average monthly T for 20.0 the period 1961-1990
15.0 Maximum average monthly T for the period 0C 10.0 1991-2006 Minimum average 5.0 monthly T for the period 1991-2006 0.0 I II III IV V VI VII VIII IX X XI XII -5.0
Figure 5. Maximum and minimum average monthly air temperatures for the period 1991-2006 compared to average monthly data for 1961-1990
The analysis of data from Ijevan meteorological station shows that for the period of 1991-2006 in 32 cases (months) the average monthly precipitation has been over 50% more that the long-term average monthly precipitation for the respective month of the period of 1961-1990.
On the other hand, for the period of 1991-2006 in 43 cases (months) the average monthly precipitation has been over 50% less than the long-term average monthly precipitation for the respective month of the period of 1961-1990.
For the period of 1991-2006 (excluding 1997, when no observations exist) in 75 months for total 180 (almost 42%) average monthly precipitation varies from the corresponding monthly data of 1961-1990 by more than 50%. Presented data proves the statement of local authorities and population about the intensification of weather extremes. Figure 6 presents comparison of the average monthly precipitation for the period 1961-1990 and corresponding maximum and minimum average monthly precipitations for the period 1991-2006 recorded in Ijevan meteorological station.
13 “Climate Change Impact Assessment” UNDP/00049248 200.0 180.0 160.0 Average Monthly P for the Period 1961-1990 140.0 120.0 Maximum Average 100.0 Monthly P for the mm 80.0 Period 1991-2006 60.0 Minimum Average Monthly P for the 40.0 Period 1991-2006 20.0 0.0
ry ly a er Ju nu May mb a March J pte e S November
Figure 6. Maximum and minimum average monthly precipitations for the period 1991-2006 compared to average monthly data for 1961-1990
5.5 Forecasted Climate Change
Recognizing that the potential consequences of a rapidly warming climate for natural and human systems are large, it becomes important to estimate the possible range of future climates we may experience over the next one hundred years.
The final report on “Model Simulations of Climate Change over Armenia Region”, prepared within SNC project to estimate the possible range of future climates the Republic of Armenia may experience over the next one hundred by the region. The assessment includes also the North-Eastern region, where Lusadzor rural community is located.
The tables below provide assessment of forecasted temperature and precipitation abnormalities in the north-east area of Armenia, compared to 1961-1990 baseline average.
Table 7. Temperature anomalies (0C) in the north-east area of Armenia, compared to 1961-1990 baseline average Year Winter Spring Summer Autumn Annual 2030 1.05 1.05 1.23 0.32 0.87 2070 2.5 2.5 2.8 1.0 2.1 2100 3-5 3-5 4-5 1-3 2.5-4.5
Table 8. Seasonal and annual precipitation anomalies in the north-east area of Armenia, compared to 1961- 1990 baseline mean (%) Area Winter Spring Summer Autumn Annual 2030 +7 +2 -9 +7 +3 2070 +15 +4 -18 +15 +7 2100 +20 +5 -25 +20 +10
‘’+’’ increase ‘’-‘’ decrease
14 “Climate Change Impact Assessment” UNDP/00049248 Climate risk assessment in the Lusadzor community showed that the climate change is an important risk factor for sustainable development of the community. Increasing variability of climate across the seasons and more extreme weather events characterize the climate change pattern in the Lusadzor community area. Intensification of extreme weather events will pose continued threat to infrastructure and economic activities of local population. Decrease of precipitation during the main vegetation period is another risk that should not be underestimated. As a bottom-up assessment showed, the frequency and intensity of floods and hails increase, and agriculture and farming practices are particularly vulnerable to that. Even though there is no hydrological observation point in Lusadzor area, according to consultations with local population, there is a noticeable decline of water resources in the area. The analysis of meteorological information also shows a trend of precipitation decline during last 10-15 years compared to 1961-1990 period. Coupled with a trend of increasing temperature, water availability will be declining in medium and long term.
The risk assessment also demonstrated poor capacity of the local communities to respond to growing risks from the extreme weather and a longer term climate change. Little awareness of adaptation options, no access to climate information and limited livelihood options constrain their response capacity to the climatic risks.
In the context of indentified risks and vulnerabilities UNDP’s development interventions needed to be assessed against the climate change related risks to their development targets. Furthermore, there was a need for further analysis to find out whether UNDP’s Lusadzor project, in its current design, would increase or decrease existing vulnerabilities in the target community. This assessment and analysis are presented in the next section of the report.
6. SCREENING OF COMMUNITY DEVELOPMENT PROJECTS ON THEIR CLIMATE CHANGE SENSITIVITY
The Lusadzor project includes activities in the following two areas: improvement of infrastructure and development of agriculture practices. Taking into account findings of interviews and meteorological data analysis (see Part 4 and Part 5 of this report), each project activity7 was screened to assess its climate change sensitivity and potential to enhance adaptive capacity in the community. Results of the screening are summarized in Table 9 below. Table 9. Analysis of community development project Project activity Sensitivity Adaptation Value Infrastructure improvement Construction Reconstruction of a part The activity is not sensitive Positive of Getahovit- of internal irrigation to forecasted climate change Given the forecasted decline of Lousadzor network in the water resources, the renovated irrigation community internal irrigation network will system; help to reduce water losses and renovation of save water the internal Construction of an The activity is not likely to Positive irrigation internal network of be sensitive to forecasted The community residents will network of the potable water (6 km) and climate change. The activity receive safe water. The village renovation of two water is limited in its scope and forecasted negative impact of reservoirs (150 m3) didn’t change the former average temperature increase
7 At the time of the report preparation some of the project activities (bridge construction, construction of 20 houses) have been already implemented, therefore, these activities have been excluded from the screening process.
15 “Climate Change Impact Assessment” UNDP/00049248 Project activity Sensitivity Adaptation Value design parameters of the two on outbreaks of intestinal daily regulatory reservoirs. infectious diseases will be Water use volume in the mitigated source will not increase as the construction of 6 km pipeline is intended only for improvement of distribution system Gasification Installation of 3 The natural gas pipes are Positive kilometers of natural gas constructed from materials Natural gas will be used for pipes that are resistant to flooding, heating houses in winter time. strong winds and other This will reduce use of wood climatic hazards from the adjacent forest School and Reinforcement of the Renovation works are done Positive kindergarten school building, according to standard Better insulation will reduce installation of new procedures for construction energy consumption windows in the school, works establishment of a computer lab and remodelling the first floor into a kindergarten with 35 places Agriculture development assistance Development Return of non-cultivated The activity will address Positive of agriculture arable land that has uneven distribution of According to climate change turned into natural irrigation water, thus forecast, the productivity of pastures back into sowing returning natural pastures pastures will reduce. Shift to cycle back into sowing cycle. As a arable land will increase second phase, water saving economic outputs from the technologies can be land, which, in turn, will introduced to promote increase incomes of farmers rational use of water and improve their coping resources, which are capacity vulnerable to climate change Planting of persimmon The climate forecast is Positive orchards and distribution positive for persimmons. Diversification of crops will of seedlings to rural However, replacement of increase the adaptive capacity households (partial traditional varieties by new of farmers. A net loss of replacement of old ones, which were not harvest due to climate orchards) previously cultivated in variability will be reduced as a Lusadsor, has to be closely result of diversified varieties monitored with stress resistant characteristics and / or with more appropriate growing cycle Distribution of female The breeding of pigs is less Positive pigs in rural households sensitive to forecasted Diversification of farm climate change as it is not livestock will increase the relaying only on natural adaptive capacity of farmers. pastures which are Diversification of the herd will considered vulnerable to reduce pressure on pastures and climate change reduce the threat of degradation of natural pastures
16 “Climate Change Impact Assessment” UNDP/00049248 7. IDENTIFICATION AND ASSESSMENT OF ADAPTATION MEASURES
7.1 Proposed Adaptation Measures
As demonstrated in Part 6 of this report, there are no immediate climate change risks to the Lusadzor project activities. However, the project is located in the disaster prone area where changes in temperature, precipitation and occurrence of extreme weather events are already pronounced, therefore integration of climate concerns in the project design is critical to ensure that the project activities are resilient to climate change impacts in the long term. Most of the project activities will have positive adaptation value. However, implementation of the project in its current design will not be sufficient to build adaptive capacity of local population. To address this, a suit of adaptation measures was proposed based on initial list of adaptations suggested by scoping exercise (Annex 2) and through consultations with the local community members, national stakeholders and donor community. These adaptation measures were then ranked within the range of 1-5 (1 - the lowest priority; 5 - the highest priority), as shown in the Table 10 below.
Table 10. Ranking of potential adaptation measures Proposed adaptation measures Rank (1-5) Comments Demonstrating benefits of adaptation measures at pilot 5 Can have long term benefits, and farms (intercropping, insulation, tillage, flood water will serve as demonstration case management etc) for cost effectiveness of climate proofing activities Transformation of non-cultivated lands to arable lands 5 Prioritized by the Government as the part of the Food security strategy implementation Improving local weather forecasting and early 5 No regret measure warning Series of workshops for farmers on climate risks to 5 Will have dual impact: increase agricultural productivity and options to adapt the awareness of local farmers on (resulting in farmer’s coping and adaptation possible climate change impacts strategies) and contribute to the knowledge transfer to local farmers Creation of communal safety nets and common 5 The cooperation of small farmers resource pools (seeds, communal irrigation) as means will greatly contribute to enforcing for coping and adaptation the coping capacity and is in line with the Rural Development Strategy of Government of RA adopted in 2006 Incorporation of adaptation measures into the 5 No regret measure integrated development plans for communities Integration of current hydrological variability and 4 The decision to implement this projections into the design of rural irrigation scheme measure has been made by the central government and the Water Agency. However, there is a need for actual enforcement and implementation of the decision Runoff capture and flood water storage facility (e.g. 4 The measure can be included in communal reservoir) as flood management and the communities mid-term communal irrigation infrastructure planning and in the upcoming plans of new Regional water management administrations Introducing persimmon as stress resistant variety for 4 Is already under implementation improving productivity of orchids
17 “Climate Change Impact Assessment” UNDP/00049248 Proposed adaptation measures Rank (1-5) Comments Introducing insulation sheets nurseries as protection 4 This has not been tested and is not against hail and frost as means to maintain diversity of an approved or available orchid yields and market security technology in the Republic. The current practice is introduction of the hail preventing systems. The proposal can be submitted to the foreign aid agencies for supporting the technology transfer. The technology should be further studied and relevance identified Promoting culturally acceptable alternative 3 There is a proposal developed by livelihoods, such as carpet making, to create income WCC Round Table Armenia buffers for adaptation Foundation on bee keeping Local financing of adaptation measures 2 Is not considered feasible in near future due to low economic potential of households and small community budget. However, the funding options need to be explored
This list was discussed with relevant stakeholders and Lusadzor inhabitants and ranked within the range of 1-5 (1 means the lowest priority and 5 means the highest priority). Based on the assessment and ranks of the adaptation measures, the initial list of measures was narrowed down to the 6 priority adaptation measures, which were subsequently analyzed and evaluated by the economist. The first measure is an extension of the planned project activity and the other five adaptation measures are additional to the initial project design.
1. Transformation of non-cultivated lands to arable lands offers an opportunity to consider current climate variability and future risks (given the increase of average air temperature and decrease of average atmospheric precipitation). Such approach is consistent with the Government’s agricultural policy to increase the domestic food production, and is particularly relevant given the need to expand employment and income opportunities for Armenia’s rural population. The Government is committed to sustain and increase the irrigated area by rehabilitating and extending the irrigation network. Moreover, the area that is thought to be potentially technically irrigable is estimated to be as much as 653,651 ha, though in 2008 only 128,000 ha were irrigated. Irrigation scheme has to be implemented in conjunction with modern water saving technologies, such as drop irrigation, and sustainable agricultural approaches, such as zero tillage, leaving crop residues in the fields to retain snow and protect soil from being eroded from winds and loosing the soil humidity. 2. Improving local seasonal forecasting and early warning (e.g. flood early warning, weather forecasting, droughts, etc.) in terms of dissemination of hydro-meteorological information. This can be done through better coordination of Hydro-meteorological Station in Ijevan, Department of Agriculture of Tavush Marzpetaran and rural community Lusadzor; Reliable data available from hydro-meteorological station in Ijevan shall be properly communicated to local population using a two-pronged approach. Firstly, people need to get the information through local TV and information leaflets. Secondly, specialists of Tavush marzpetaran and administration of Lusadzor community should help farmers understand what to do with the information provided. 3. Introducing farm piloting/demonstration of adaptation measures (e.g. intercropping, tillage practice, efficient irrigation scheme, new stress-resistant varieties (preferably local endemic varieties), testing seasonal protection infrastructure against hail and frost, etc).
18 “Climate Change Impact Assessment” UNDP/00049248 4. Facilitation of community meetings and farmer trainings on climate risk management to identify and help develop farmers’ coping and adaptation strategies. 5. Creation of communal social nets and common pools of resources (e.g. common pool of seeds and seedlings, communal irrigation management, etc) to help cope with major stressors in the community. This adaptation measure is proposed given the increasing incidents of evidenced and anticipated climatic hazards, such as hails, strong winds, floods and droughts. 6. Consideration of climatic hazards in infrastructure development works as part of community development plans. This can be done through ensuring that during the planning and design of the main infrastructures it is necessary to take into consideration the climatic hazards through the use and analysis of hydro-meteorological information. This adaptation measure has to be communicated to higher level authorities, since it has significant budget implications (most of the rural infrastructure works are financed from the central government budget).
7.2 Economic Assessment of Adaptation Measures
7.2.1 Economic Assessment of Adaptation Measures
The suggested adaptation measures were analyzed against the following four criteria8:
1. Effectiveness as a solution to problems arising from climate variability and change (benefits, damage avoided or mitigated, losses and associated costs avoided or minimized); 2. Cost – additional cost of adaptation measure compared to cost of not modifying the project; 3. Adequacy for current climate – benefits under the current climatic conditions and future scenarios; 4. Technical feasibility – capacity and means to implement the measure.
The assessment against criteria 1, 3, 4 has been done by the project expert in consultation with UNDP and local communities using expert judgment and local knowledge. In order to assess costs of the adaptation measures, a detailed analysis has been performed by an economic expert. This analysis is presented in the sub-chapter 7.2.2 below.
The assessment was done using the following scale: + low, ++ medium, +++ high, with the exception of the cost criteria, where reverse order was used: +++ low; ++ medium, + high.
Table 11. Assessment summary
Adaptation measure Effectiveness Cost Adequacy for Current Climate Technical Feasibility 1. Expansion to the non-cultivated arable land ++ + ++ + 2. Improving local seasonal forecasting +++ +++ +++ +++ 3. Introducing farm piloting/demonstration for adaptation measures +++ + +++ ++ 4. Community meetings and farmer trainings +++ +++ ++ ++ 5. Creation of communal social nets and common pool of resources +++ ++ +++ +++ 6. Consideration of climatic hazards in infrastructure development works ++ +++ ++ +++
8 This approach was suggested by the screening exercise (Annex 2)
19 “Climate Change Impact Assessment” UNDP/00049248 7.2.2 Cost Assessment of the Proposed Adaptation Measures
For cost assessment of adaptation measures findings of various works of similar nature have been used, as well as baseline data provided by the rural community administration. Particularly, methodologies applied by Millennium Challenge Corporation – Armenia (MCA) have been used in some calculations. Alternatively, for same adaptation measures the method of analog calculation has been used. As for assessment of economic efficiency of plant growing, as well as impact of climate change on plant breeding, the structure of the main plants grown in Lusadzor community, as well as their yield and net profit from 1 ha (for irrigated and non-irrigated lands) was studied.
The total budget of implementation of the proposed adaptation measures is approximately $213,000. It should be noted that for some measures the budgets are approximate and more comprehensive assessment would be needed at the design drawing phase of the corresponding adaptation measures.
The detailed economic analysis for each of the adaptation measures is provided below.
1. Transformation of non-cultivated lands to arable lands
Costs
After construction of Getahovit-Lusadzor gravity irrigation system9 it is suggested to construct a new pipeline of 1.8 km length from Duzer canal (project designed by MCA Armenia), which will make it possible to irrigate additional 25 ha of plough-land and about 24 ha of other land.
In order to calculate the precise cost of the project it is necessary to conduct design and engineering works. The table below provides a budget estimate, which was done based on costs for similar types of works. The total cost for turning 1 ha to arable land will amount to about $1,653.
Table 12. Budget of the proposed adaptation measure
No Item/Type of work Quantity Unit Cost, $ Total, $ 1 Design-calculation works 1 5,500 5,500 2 Construction works 1.8 km 39,000 70,200 3 Contingency 7% - 5,300 Total 81,000
Benefits
In order to assess the economic benefits resulting from turning non-irrigated lands into irrigated lands, data on land productivity and net profit from 1 ha in Lusadzor Community has been analyzed (see table 13).
Among vegetables, tomatoes are the dominant type, and among fruits – blood orange and fig, thus in calculating net income from 1 ha of land, these sorts are taken as basis. Corn, vegetables, potato, blood orange and fig are not processed in non-irrigated lands due to high risks. It should be noted that the processed grain and perennial plants from non-irrigated lands are dominantly used by farmers for their own needs. Often grain is also used for feeding livestock due to its poor quality.
9 MCA Armenia is ordered to design gravity irrigation system Getahovit-Lusadzor, which will make it possible to irrigate 200 ha of lands through gravity system, of which 60 ha is located in Lusadzor community (currently non-irrigated). This system will replace the pump stations Getahovit-Ijevan and Getahovit-Lusadzor, which are inoperational since 1992. It is also envisaged to rehabilitate Duzer canal, which will provide for continuous water supply of additional 100 ha of land.
20 “Climate Change Impact Assessment” UNDP/00049248
The community receives annually approximately $215,000 net income (average $1,091 per farm, and $300 per capita). As it can be seen from the table above, the efficiency of irrigated lands is 6.4 times higher than of non-irrigated. Thus, transformation of non-irrigated lands into irrigated lands would generate additional income of about $2,277 per 1 ha of land. It should be noted that transformation of non-irrigated lands into irrigated lands should be followed by introduction of water saving technologies.
Table 13. Distribution of crops, yield and net income from 1 ha of land in Lusadzor Community (2007)
Irrigated and non- Irrigated and non- Net profit from 1 Yield irrigated land, ha irrigated land, % ha (in USD) (tons/year) Plant/crop Non Irrigated Non Non Non type Irrigated Irrigated Irrigated irrigated land irrigated irrigated irrigated land land land land land land land Grain 70 - 56 - 340 670 1 3-4 Corn - 20 - 33 - 2,300 - 3 Vegetables 54 6 - 10 - 2,700 - 15 (tomato) Potato - 6 - 17 - 3,600 - 35-40 Perennial - 10 44 10 530 1,600 4 8 plants Fruits (blood - 18 - 30 - 3,000 - 3.5 orange, fig) Total 124 60 100 100 424 2,701
It should be noted that these indicators may vary significantly every year. This is contingent upon the yield of crop processed in the given year in the Republic, market demand in the country, government policy and other factors. For example, in previous years the community was largely processing tobacco, which was considered a profitable business. There was a company, which signed advance contracts with farmers and purchased the entire harvest at high prices. However, recently the company has significantly reduced the price of raw tobacco, which caused decrease of income from processing tobacco and currently the community does not produce it anymore.
2. Improving local seasonal forecasting and early warning
The program does not require significant financial investments. Currently the cheapest and most efficient method for increasing public awareness on hails, mudflows, droughts and other climatic hazards is through telephone, TV, printed brochures and information leaflets. Information leaflets can be printed once in a quarter with provision of one copy to each household.
Table 14. Budget of the proposed adaptation measure
No Item/Type of work Quantity Unit Cost, $ Total, $ 1 Booklets 800* 0.9 720 Total 720 * 200 examples per 3 month.
21 “Climate Change Impact Assessment” UNDP/00049248
3. Piloting adaptation measures at farm(s)
3.1. Training courses on agricultural methods
Currently the farmers do not always process correct crop types, and often misuse the land areas, violate irrigation norms and regimes, use fertilizers irregularly, do not use agricultural machinery appropriately, as a result, yield of crop decreases and erosion processes are observed on slopes. Thus, it is suggested to implement a training course for farmers on appropriate methods of agricultural practices. The duration of the proposed training is two months (3 phases, each of which is 10 days, 2 lectures per day). The table below presents the budget of the proposed measure.
Table 15. Budget estimate for training on sustainable agricultural methods No Item/Type of work Total, $ 1 Trainers salary (12 man/day x $ 50/day) 600 2 Transport (2 times x 350 km x $ 0.6) 420 3 Accommodation (2 person* x 8 days x $ 40) 640 4 Training materials (90units x $ 5) 450 5 Contingency (7%) 148 Total 2,558 *Driver included.
3.2 Introduction of drip irrigation
In some territories of the country MCA Armenia has implemented introduction of drip irrigation systems. According to the most modest calculations, after the introduction of the system water savings increase 3-5 times. Thus, it is proposed to construct a drip irrigation testing system in Lusadzor rural community on the area of 5 ha of land. The experience shows that the average cost of introduction of such system on 1 ha of land is about $6,000.
In order to calculate the precise costs of the project it is necessary to conduct detailed design works. The table below provides approximate budget for the proposed measure.
Table 16. Budget estimate for introducing drip irrigation No Item/Type of work Quantity Unit Cost, $ Total, $ 1 Design-calculation works 1 4,500 4,500 2 Construction works 5 ha 6,000 30,000 3 Contingency 7% - 2,415 Total 36,915
3.3 Introduction of anti-hail system
In 2007 in 3 communities of Shirak marz of Armenia (Voghji, Haykavan and Meghrashat rural communities) anti-hail systems “Zenith” were installed. In each community 2 systems were installed, and the service area of each anti-hail station is 80 ha. All the stations are automated and managed from the Gyumri center.
Similar system can be introduced in Ijevan region. The costs of installation of one station including maintenance costs (excluding centralized management unit) are presented below:
Thus, if we assume that one station can serve 80 ha of land area, then the required investment costs for 1 ha will add up to $970, and the annual maintenance cost of the station will be $33/ha. It should
22 “Climate Change Impact Assessment” UNDP/00049248 be noted that in Lusadzor community on average $1,175 net income is generated from 1 ha of land. The operation of anti-hail station will significantly reduce the risks associated with hails and prevent potential economic losses.
Table 17. Budget estimate for installation of anti-hail system No Item/Type of work Total, $ 1 Anti-hail station (1 unit x $ 70000) 70,000 2 Operator-specialist (1 person x $ 200/month x 12 months) 2,400 3 Acetylene gas ($ 0.5 x 80 ha) 40 4 Contingency (7%) 5,071 Total 77,511
4. Facilitation of community meetings and farmer trainings on climate risk management
The proposed duration of the training classes is two months (3 phases, each of which is 10 days, with participation of 3 trainers). The table below provides a rough budget of the proposed measure.
Table 18. Budget estimate for training on climate risk management No Item/Type of work Total, $ 1 Trainers salary ( 12man/days x $ 50/day) 600 2 Transport (3 times x 350 km x $ 0.6) 630 3 Accommodation ( 12 days x $ 40) 800 4 Training materials (90 units x $ 5) 450 5 Contingency (7%) 174 Total 2,654
5. Creation of communal social nets and common pool of resources
The proposed duration of the training classes is four months with participation of 2 professional trainers (duration of each training session is 10 days). The table below provides a rough budget of the proposed measure.
Table 19. Budget of the proposed adaptation measure
No Item/Type of work Total, $
1 Trainers salary (4 man/days x $ 70/day) 280 2 Transport (2 times x 350 km x $ 0.6) 420 3 Accommodation (4 days x $ 40) 160 4 Training materials (50 units x $ 5) 250 5 Contingency (7%) 77 Total 1,187
6. Consideration of climatic hazards in infrastructure development works
The community development projects should be checked according to their climate change sensitivity. For that purpose it is suggested to develop guidelines and build an advisory team consisting of specialists from Hydromet agency, including agro-meteorologist, hydrologist and
23 “Climate Change Impact Assessment” UNDP/00049248 experts from Agricultural Academy. The guidelines will be used to ensure that during the planning and design of the main infrastructures it is necessary to take into consideration the climatic hazards through the use and analysis of hydro-meteorological information. The entire process has to be communicated to higher level authorities, since it has significant budget implications given the fact that most of the rural infrastructure works are financed from the central government budget.
7.3 Status of Implementation of the Proposed Adaptation Measures
As already mentioned, six adaptation measures have been proposed for Lusadzor community. Among them, adaptation measures 1 and 5 are already being implemented by MCA and UNDP.
Particularly, regarding the proposed adaptation measure 1 “Transformation of non-cultivated lands to arable lands”, the Millennium Challenge Corporation – Armenia has already completed the design of Getahovit-Lusadzor gravity water supply system, which will make it possible to irrigate about 200 ha of land area, of which 60 ha is in Lusadzor. The system will replace Getahovit-Lusadzor pump stations.
The proposed adaptation measure 2 “Improving local seasonal forecasting and early warning”, does not require significant financial resources. It can be done quite easily through improvement of coordination between the hydro-meteorological station in Ijevan, Department of Agriculture of Tavush marzpetaran (regional administration) and the leadership of Lusadzor rural community. Production of simple weather forecasting brochures and establishment of information exchange mechanisms are some of the tools to achieve the objectives.
It is suggested to focus the proposed adaptation measure 3 “Piloting adaptation measures at farm(s)” on organization of drip irrigation. This method has been tested in several regions of the country with the support of the Millennium Challenge Corporation - Armenia. According to an expert assessment, the introduction of drip irrigation system will allow to use at least 3-5 times less water. The average cost of introduction of drip irrigation system is approximately $6,000 per one hectare.
The proposed adaptation measure 4 “Facilitation of community meetings and farmer trainings on climate risk management” can be implemented through trainings that may focus on how farmers are already responding to the risks, what works well and what not and why, what are the primary needs to improve adaptive capacity and minimize damage and losses.
Regarding the proposed adaptation measure 5 “Creation of communal social nets and common pool of resources”, UNDP is already providing garlic seeds to the community and supports the establishment of common resource pool. In addition, detailed training is provided on processing methods and best practices.
The proposed adaptation measure 6 “Consideration of climatic hazards in infrastructure development works” can be implemented with the support of meteorologists and hydrologists from Ijevan hydro-meteorological station.
The table below summarizes the economic assessment of proposed adaptation measures, as well as status of their implementation.
24 “Climate Change Impact Assessment” UNDP/00049248 Table 20. Summary table of adaptation measures No. Proposed Measure Budget Status Donor/Potential Donor 1. Transformation of non-cultivated 81000 Under implementation MCA Armenia lands to arable lands 2. Improving local seasonal 720 Proposed for joint WB, ADPC, UNDP forecasting and early warning, and implementation by particularly flood early warning, local authorities, weather forecasting, droughts and marzpetaran and etc. specialists from Ijevan hydro-meteorological station 3. Introduction of farm 124837 Proposed for IFAD, CARD, USDA, piloting/demonstration for implementation WB adaptation measures 4. Facilitation of community 2558 Proposed for WB, ADPC, UNDP meetings and farmer trainings on implementation by climate risk management to local authorities identify and help develop farmers’ coping and adaptation strategies 5. Creation of communal social nets 2654 Being implemented UNDP Armenia and common pool of resources to help cope with major stressors in the community 6. Consideration of climatic hazards 1187 Proposed for DFID, GTZ, OBCE, in infrastructure development implementation with UNDP works the support of meteorologists and hydrologists from Ijevan hydro- meteorological station Total 212956
25 “Climate Change Impact Assessment” UNDP/00049248 CONCLUSIONS
It has been recognized that the Integrated Development Plan for Lusadzor Community project is not “climate-risk neutral” as it has the potential, through its local development planning and pilot actions, to decrease local vulnerability to climate change impacts. By establishing the internal mechanism for climate risk management the project can attract donors and raise awareness among the local governments and population, at large, about climate risks to local development. Importantly, even though there are no immediate climate risks to the project activities, the project is situated in the disaster prone area where climate change is an important risk factor for sustainable development of the community, therefore integration of the climate concerns in the project design is critical.
Six adaptation measures have been prioritized as an outcome of this study. Two of the proposed measures are being implemented by MCA Armenia and UNDP. Resource mobilization and cooperation with local authorities are recommended to fund the implementation of the other four measures.
The following lessons learned/recommendations may be useful for future similar initiatives:
1. The process was highly time-consuming, which may be explained by the participatory process. Also, this was the first climate proofing exercise and a lot of time and efforts were spent on development of the methodology. This should not be necessary when replicating the experience. 2. In the process of project screening it was concluded that some of the project activities have inherent adaptation value, which was important to identify before recommending any additional adaptation measures. For the future climate proofing initiatives, it is recommended that at the screening stage existing and/or proposed adaptation strategies, relevant to the project/project area, are identified and taken into consideration when planning for adaptation response. 3. Even if project activities have more adaptation value than are subject to climate risks, there is always a scope to improve adaptive capacity. Developing adaptive capacity helps increase effectiveness and longer term sustainability of the project. 4. It is important to focus on cost-effective and feasible measures, but even then adaptation measures add on to the project costs. Therefore, they should be designed in a way to reinvigorate the project objective rather than reroute the project on a new track. 5. It is highly recommended to introduce climate risk management at the project design stage, as part of feasibility, so that the additional cost is fully reflected in the project budget. 6. Disaster risk reduction measures and disaster preparedness are not dealt with in this report. However, the links between climate change adaptation and disaster risk reduction are increasingly apparent and the negative implications on food security and livelihoods are high, especially in an area like Lusadzor where a large part of the population depends on agriculture. Changing climate patterns thus increase the urgency to invest in disaster risk reduction, disaster management and preparedness. It is suggested that other climate proofing initiatives pay close attention to this issue.
26 “Climate Change Impact Assessment” UNDP/00049248 ANNEXES
Annex 1. Technical Report based on findings during the mission to Yerevan 11-14 September 2007
Reasons for climate proofing:
Socio-economic development does not reduce vulnerability unless specifically and explicitly addressed in development policies, plans and projects. Moreover, it has been increasingly recognized that the development processes can actually increase vulnerability and exposure to risks when climate change impacts are overlooked. Therefore, win-win solutions need to be sought. Such solutions are best identified by integrating climate change adaptation strategies into the overall development framework. Integration of climate change adaptation won’t overload mainstreaming agenda of development planning if it becomes part of development assistance programming routine. The purpose of the UNDP Country Office support in Armenia is to assist the programme developers of UNDP and other UN agencies to routinely mainstream climate change adaptation into the country assistance programmes and plans (CPAP, UNDAF) in order to ensure their sustainability and long lasting impact. The core rationale of this effort is to pilot and establish institutional response system within UNCT and UNDP in particular that will facilitate the policy change and capacity development to achieve MDGs in the face of Climate Change.
Armenia’s climate change profile – key risks and vulnerabilities
Extreme weather events and disasters - Climate change in Armenia has been pronounced by more frequent and severe weather events, such as droughts, spring frosts, hails, floods, mudflows, winds and forest fires. During the past decade extreme weather events have been recorded to accelerate. Recorded number of strong hails during 2001-2006 reached 46 cases (diameter 22-35mm, on average); Heavy floods during the past decade even resulted in human losses. In 2000 losses from droughts in the sector of agriculture made up $66,7 mln., constituting 10.1% of agricultural gross product. This includes 35% share of potato yield, 20% of- cereals, and 16% of - vegetables. In 2005 the crop yield loses from hail, floods and frost combined made up about $15 mln.
Climate aridation and changes in biota - The research based on observations from 50 meteorological stations during 1930-1990 shows that the average annual precipitation has decreased by 5.8% and temperature has increased by 0.7°C in Armenia. This is expected to worsen in light of anticipated climate change scenarios. Climate aridation is already felt in arid Armenia. During the last millennium the forest areas have significantly reduced, the semi-desert and steppe vegetation belts have expanded and the Alpine vegetation belt has reduced. Increased occurrences in forest fires and pest outbreaks are another consequence of the climate aridation that negatively impact forests in Armenia. At present, the forest area damaged annually by insects is approximately 14,500ha (on average).
Land productivity - According to the climate change forecasts the humidity of soil will decrease by 10-30% by mid century and subsequent moisture deficit will impact the plants’ growing capacity. According to the First National Communication the productivity of cereals will be reduced on the average by 9-13%, vegetables – by 7-14%, potatoes – by 8-10% and horticulture by 5-8%.
Water stress - The observed climate warming will impact the runoff formation that largely depends on snow cover in Armenia. The water reserves in snow on whole territory have already
27 “Climate Change Impact Assessment” UNDP/00049248 decreased by 5-10% during the baseline period (1961-1990). Water balance in Lake Sevan is also affected by the increasing evaporation from its surface.
Health issues - It is also forecasted that the rate of cardio-vascular system diseases will increase, especially among the risk groups. Increase in number of malaria morbidities has been detected (e.g., in 1998 the number of people contracting the three-day malaria reached 1156). Increase in number of cholera vibrio of group 01 (El Tor vibrio) from 1.4 to 2.4% in Armavir region of Armenia during 1998-2001.
UNDP programme priorities:
UNDP in Armenia pursues a long term objective of achieving MDGs by focusing on three strategic objectives: • Laying the foundation for sustainable socially-oriented growth; • Promoting accountable, transparent and effective governing institutions; and • Supporting sound management of natural resources.
These programme priorities are addressed in programmes and projects clustered around the three main thematic practices: Economic and Social governance, Democratic Governance and Environmental Governance. Initial screening of the UNDP country portfolio in Armenia has identified number of projects particularly susceptible to climate risks that have to be subject to a full-fledged mainstreaming of the CRM.
Community Development Programme:
UNDP’s one of the flagship programmes in Armenia that has mobilized governmental commitment and donor interest is a Community Development Programme. The core objective of the programme is to improve local livelihoods and minimize incidents of poverty. The project targets communities and clusters of communities in bordering regions (Marz) and villages, that are at high risks of social and cross-border conflicts. UNDP’s community development programme applies highly decentralized approach to community participation and mobilization in order to prioritise local actions in the framework of individual pilot initiatives. Participatory Rural Appraisal (PRA) has been applied for participatory planning and prioritization. Local development plans have been developed that have a direct link with the communities’ budgets. The programme is introducing performance budgeting in number of target communities with the objective to maximize availability of limited resources and optimize income and expenditures. The pilot projects focus on rural infrastructure (rehabilitation of schools, local irrigation schemes, roads etc). The project also enforces the principles of communal management by creating the communal pools of assets (i.e. local irrigation, agricultural machinery, seeds etc). Situation analysis in certain target areas (Tavush, and Syunik) has recognized extreme weather events and climate disasters, such as floods and droughts, as important risks interrupting local development schemes. However, no targeted efforts have been made to address these risks in local planning and pilot activities.
Regions are strongly dependent on agriculture. To different extent, adverse climatic events (e.g., floods, droughts, and hails) can have negative repercussions for the economy of each region, through impacts on food production and local rural infrastructure. Without conscious efforts to adapt potential increases in the frequency or in magnitude of adverse climate events or changes in climate averages may make it more difficult for some communities to participate in the rural economy.
28 “Climate Change Impact Assessment” UNDP/00049248 Main climate risks in target regions:
Based on consultations with the SNC experts and the data currently available to the project team the following extreme weather events have been detected in the target regions.
Tavush – hails floods, mudflows Aragatsotn – aridization in mountainous areas and foothills, hail and droughts, particularly vulnerable as a region of rain-fed agriculture; Syunik – droughts, forest fires and pest outbreaks; Shirak – floods, hail, droughts; Gegharkunik – Sevan region, drought, seasonal, spring floods, frost; Ararat – aridation trend; Armavir –
Suggested actions for CRM Mainstreaming:
Having recognized that climate change can significantly weaken the efforts to reduce poverty levels, UNDP in Armenia is committed to address current and future vulnerabilities by designing and implementing preventive adaptation measures. It has also been recognized that Community Development Programme is not “climate-risk neutral” as it has the potential, through its local development planning and pilot actions, to either increase or diminish vulnerability to climate change impacts. On the other hand, by establishing the internal mechanism for climate risk management the community programme can attract donors and raise awareness among the local governments and population, at large about climate risks to local development.
The following actions will help consider direct and indirect impacts of climate change on the programme objectives: • Assessment of current status of vulnerability and adaptive capacity; • Identification of main “hot spots” – geographic areas, Marzes and communities; • Design and implementation of adaptation measures; • Addressing adaptation into the communities’ integrated development plans; • Incorporation of adaptation costs (at least partial) into the performance budgeting schemes; • Including climate risk exposure and vulnerability among the criteria to prioritize communities for future local pilot actions;
29 “Climate Change Impact Assessment” UNDP/00049248
The following technical guidance can be used for risk and vulnerability assessment. A study is required that will consider farmers’ adaptive capacity and their livelihood sensitivity to climate events as two key attributes of vulnerability. • PRA has to incorporate set of target questions during the assessment / planning phase: 1. Perception of community about frequency and intensity of extreme weather events and climate hazards (floods, mudslides, droughts, hail etc); 2. Type and magnitude of losses due to these hazards (human losses, damage to infrastructure, economic losses such as crop failures etc) 3. Assessment of key vulnerabilities, current coping mechanisms and capacities; 4. Priority needs to address additional vulnerability induced by climatic risks and actions for risk reduction.
Scope of work: • Design a survey to collect data on the following categories: o farm characteristics: type of production system; landholding size; agricultural practices; main sources of income; o farm-level resources hypothesized to be associated with adaptive capacity: main age groups; access to technology and use, access to climate information and use; risk perception, access to finances; o farm households’ sensitivity to climate impacts frequency and extent of crop losses (based on data from the survey, as well as from interviews), types of adjustments farmers have reported making to current climate risk; and obstacles faced in incorporating such adjustments; • Design an interview protocol to explore farmers´ risk perceptions and their attitudes regarding coping with stresses from extreme weather and average changes in local climate; • Identify how farmers view the primary climate extremes and how they perceive their sensitivity to them; • Identify and record current climate risk management strategies of farmers, such as: o crop diversification and seasonal crop switching; o changes in tillage practices (reduced tillage); o current management capacity: crop diversity, and farmer’s access to alternative non-farm income sources; availability of compensatory mechanisms in case of crop loss and other support financing; availability and use of climate information; availability of extension services; • Design adaptation, including adaptive capacity development, measures for target communities; Improve availability of climate information and early warning; Farmers training on low tillage, cropping / intercropping practices, water harvesting etc; Support to livelihood diversification options that minimize reliance on climate sensitive natural resources (land);
30 “Climate Change Impact Assessment” UNDP/00049248 Creation of social safety nets and pool of resources that will help withstand major climatic stresses (droughts, floods, hails and frosts); Incorporation of adaptation measures into the three year integrated development plans for communities; Addressing adaptation costs in local budgets to develop local adaptive capacity (introduction of local drought resistant crops / varieties, local water reservoirs, flood management etc)
Key elements of methodology for consideration:
• Assessment of socio-productive structures in target regions is a critical starting point for vulnerability and adaptive capacity assessment; • Any interventions intended to enhance adaptation to climate risk need to be considered in the context of the opportunities and constraints posed by the broader institutional environment and, conversely, there is a need to examine closely how institutions and policy explain differential adaptive capacities at the farm level; • Understanding the existing coping and adaptive strategies of farmers in specific geographic contexts is thus a first step toward the identification of appropriate options to increase the potential for adaptation of particular farmer groups; • Local-level analyses also can help highlight the primary constraints to adaptation and the differential nature of vulnerability of particular groups; • Local-level analyses can help prioritize adaptation interventions and thus facilitate the creation of a more sustainable and equitable production environment; • Focus on both qualitative and quantitative data pertaining to farmers’ specific actions in response to climate stress and their perceptions of the constraints associated with these coping and adaptation strategies; • The project can involve a farm survey, interviews, and workshops with farmers and other actors in the agricultural sector (public officials, communities, rural infrastructure specialists, and climate experts) in target regions. • Improved access to climate, market, and technological information is an important means for enabling the farmers to respond rapidly to economic and environmental change. Enhancing the accessibility of information is a viable adaptation measure (Based on AIACC Working Paper No. 39 September 2006)
Cooperation and synergies with UNDP/BCPRs disaster risk reduction project
It is important to ensure a methodological consistency for local level adaptation across all local community and area-based programmes of UNDP. Therefore, the project can largely benefit from closer interaction and partnership with the UNDP/BCPR funded project: “Strengthening of National Disaster Preparedness and Risk Reduction Capacities”. The project is at incipient phase of implementation targeting 90 communities of the Ararat Marz.
The project intends to apply Vulnerability and Capacity Assessment (VCA) methodology developed by the Red Cross. This internationally recognized methodology for disaster reduction and preparedness can be viewed as a first step towards longer term adaptation approaches at local level. VCA methodology can also be tested and applied as part of the PRA based situation analysis in the framework of the community programme.
Further consultations are required (SNC experts, BCPR, Red Cross, etc) to adopt a best fitted methodological approach that in addition to disaster risks assessment will accommodate climate risk and longer term adaptation needs (see the suggested TOR and methodology above)
31 “Climate Change Impact Assessment” UNDP/00049248 Localizing MDGs:
In the context of decentralization processes in Armenia, and in order to address regional development challenges and bring MDG’s to communities, MDG localization initiative was launched, which will be synchronized with PRSP revision process. UNDP will support Armenia’s efforts in development of MDG regional frameworks that will be used for regional / community development plans. This approach is based on necessity of including marz perspectives. It is expected that the results of MDG localization will be incorporated also in the revised PRSP. This initiative offers an opportunity to capture adaptation needs into the local development frameworks.
Exposure to climate risks is one of the constituent elements of human vulnerability as socially disadvantaged and the poor have the least coping capacity to climatic hazards that interrupt their development schemes. Therefore, the following actions can be suggested for CRM mainstreaming:
A comprehensive situational analysis is planned to cover socio-economic, governance and environmental issues. The assessment should consider vulnerability to climate change, especially in such risk prone areas (Marz), as Shirak, Aragatsotn, Ararat, Tavush and Gegharkunik.
MGD-based assessments for poverty reduction and environmental sustainability should particularly prioritise water stressed regions, with observed tendencies of further aridization of climate and desertification, loss of land productivity due to loss of soil moisture and extended droughts. The regions exposed to impacts from floods, landslides and hail should be prioritized too.
A set of prioritization elements for identification of a pilot region can help address vulnerability to climate risks in conscious manner: • Arid region heavily reliant on agriculture; • Region with non-irrigated / rain-fed agriculture; • Water stressed with poor land productivity; • High seasonal fluctuations in precipitation leading to seasonal floods and landslides;
Immediate next steps:
• Following the guidance provided above undertake the actions for CRM integration into the Community Programme; o Incorporate climate risk as part of the criteria for selecting priority communities for local pilots; o Develop TOR (scope and methodology provided above) for risk and vulnerability assessment, as an integral part of the PRA; o Test the assessment in the currently pipelined region (e.g. Shiraz Marz) o Retroactively undertake the assessment in the Tavush and Syunic marzes where the initial vulnerabilities and risks have already been identified (flood and droughts respectively). • Ensure methodological consistency and close coordination between the community programme and BCPR’s disaster risk project (especially, the parts on vulnerability assessment and community actions / pilots); • Following the guidance provided above incorporate vulnerability to climate change and adaptation needs as part of the local MDG reports; o Incorporate vulnerability to climate change as part of the criteria for selecting a pilot region for localizing MDG reports (see above); o Develop the TOR for risk and vulnerability assessment, as part of the situational analysis;
32 “Climate Change Impact Assessment” UNDP/00049248 o Involve SNC experts and V&A reports into the technical studies of the local MDG reports; o Incorporate adaptation needs as part of the poverty targets.
National Priorities for Adaptation:
As a result of consultations with the SNC project team, UNDP CO Environmental governance unit and national counterparts the following key sectors and entry points have been identified to offer the opportunities for adaptation mainstreaming in Armenia.
Decisions on water use do not consider the dynamic of change during the past 10 years that indicate some of the alarming trends of reduction in runoff formation. Potential conflicts between the irrigation, small hydro power generation and municipal water supplies are anticipated. Out of 1200 settlements 600 are covered by the centralized water supply system, another 600 are largely rural communities of rain-fed agriculture and remote mountainous villages reliant on local sources of water. The government of Armenia adopted the water code in 2002 and a comprehensive water programme in 2005. None of the frameworks take expected water stress into account. This is quite paradoxical in the light of the fact that the water agency is part of the Ministry of Environmental Protection that also hosts the National Communication project. As part of the water programme, 70 water reservoirs of various capacities are planned to be built without any considerations of climate change impacts on water resource, both in terms of quantity and quality.
Currently, a new strategy for hydropower sector is being developed for over the next 25 year timeframe. The National Communication project team has established a solid cooperation with the key stakeholders at the Ministry of Energy. This offers the opportune grounds for considering precipitation and runoff formation trends into the 25 year strategy for hydropower generation that currently accounts for 30% of total energy production in Armenia.
Agriculture is another production system of particular vulnerability to climate change. In 2000 the losses from the droughts in agriculture made up $66,7 mln. Drought impacted the sector in 2006 too. Droughts, hail and frost are key weather events making a highly fragmented and predominantly smallholder agriculture of Armenia particularly vulnerable. Due to seasonal shifts, precipitation fluctuations and spring frosts pose discernable impact on horticulture production for the past 5-6 years. Upward shift of production zones from 600m to 1200-1800m is being observed. Sustainable Development Strategy of Agriculture recognizes importance of such actions as forecasting and prevention of climate disasters (floods, droughts). However, there is no clear formulation of climate change risks to the sector in the medium and long term, neither the adaptation measures identified.
Armenian forests are also vulnerable to climate change impacts. Climate aridation, particularly pronounced in the southern parts of the country, lead to vertical shifts of the climatic zones leaving limited coping range to the forest ecosystems. Frequent forest fires and pest outbreaks damage valuable oak forests of the south-east. National forest strategy has already recognized additional stress posed to sustainable forest management in Armenia. However, the needs for more robust vulnerability assessment and implementation of adaptation measures remain unresolved.
Barriers:
• One of the main barriers stated is inaccessibility of relevant data for decision-making; o Existing data generated by the Hydrometeorological service is largely a raw material; o Information, if available, is not readily shared as viewed as economic asset to be held until some funding opportunity arises;
33 “Climate Change Impact Assessment” UNDP/00049248 o Climate change projections and vulnerability related data and information have not been made accessible to policy formulation, especially in the climate sensitive sectors (agriculture, water, forests); • There are no legislative and procedural requirements for incorporation of adaptation needs into the policy formulation, strategic planning and programme development; • Mandate of certain agencies contain clear conflict of interest - on the one hand, the interest of issuance of licenses on use and on the other hand the interest of conservation, and rationale use (water agency of the Ministry of Environmental Protection); (more detailed barrier analysis is currently underway in the framework of SNC project)
Immediate next steps:
• Closely monitor and support implementation of the Second National Communication that should provide data and analysis about the key vulnerabilities and climate risks in Armenia; • Support SNC’s vulnerability and adaptation (V&A) studies as they are to inform UNDP’s programming framework as well as policy advice to the national government; • Ensure the quality of baseline study of climate risks and trend analysis (including impacts) to be produced by the SNC by end of October; • Based on the baseline assessment findings initiate the dialogue with the government of Armenia about integrating climate change risks into the following sectorial policies and programmes (see also the attached work plan):
Name Type Status Stakeholder Comments Agricultural Sectorial policy Approved in Ministry of Sustainable 2006; subject to Agriculture Development Strategy revisions Programme of Programme On-going WB, Ministry of With a ratio of 70/30 co- experimental farming Agriculture financing WB and local farmers fund the experimental farming activities to test and identify the best farming options (including optimal varieties, cropping models etc). This programme can offer a great deal of opportunity for identification and implementation of adaptation measures. Scheme/Strategy of Sectorial policy On-going; tender Ministry of Old strategy was developed hydropower has been Energy, WB, in 1997. Hydropower development announced to EBRD. generation plans and undertake a investment decisions were review based on the old scheme. The new scheme should consider precipitation projections and trends of water runoff formations. 25 year timeframe offers a unique opportunity for climate change adaptation mainstreaming National Forest Policy Sectoral policy Approved in Ministry of and Strategy 2004; subject to Agriculture revisions Forest National Programme Approved in 2005 Ministry of Programme Agriculture
34 “Climate Change Impact Assessment” UNDP/00049248 Name Type Status Stakeholder Comments Water code Law Approved in Ministry of 2002; subject to Environmental revisions Protection; Water Agency Water Programme Programme Approved in Ministry of 2005. on-going Environmental Protection; Water Agency; Water committee Rural water Programme Under preparation Water committee The Japanese government programme plans to support water supply improvements to the settlements that are not covered under the centralized water supply system. This offers a great opportunity for UNDP to partner with the Japanese funded programme and integrate adaptation measures for improved water supply in rain-fed agricultural lands and local traditional water use National Policy and Plan Under preparation Ministry of Currently, UNDP supports Environmental Action Environmental the formulation of NEAP, Plan Protection. which has taken an Sectorial approach of sectorial Ministries analysis and plan of action. Climate risks and adaptation measures should be fully reflected into the NEAP under each sectorial section and action plan.
35 “Climate Change Impact Assessment” UNDP/00049248 Annex 2. Notes to the project TOR Lusadzor Community: Integrated Development Plan
Background: Community Development programme of UNDP CO in Armenia has been identified as a priority to introduce climate risk management system. The main rationale is that the programme supports local development in climate sensitive areas. Situation analysis has recognized extreme weather events and climate disasters, such as floods and droughts, as important risks interrupting local development schemes. However, no targeted efforts have been made so far to address these risks in local planning and pilot activities. Therefore, the main objective of this exercise is to integrate climate risks management into the Lusadzor community project.
Problem Diagnosis: Initial scoping exercise identified Tavoush Marz (where Lusadzor is located) as one of the disaster prone regions of Armenia. In particular, occurrences of floods, hails, frosts and mudflows constitute main climatic disasters. Vulnerability to disasters has also been recognized by the project TOR (table 2). Low agricultural productivity, arable land abandonment (due to poor irrigation and access to arable lands among the main reasons), and poor rural infrastructure (housing issue, roads and bridge to connect to the area with arable land) are among the top priority issues for the local community development. High climatic variability and future changes in mean parameters may exacerbate the current development challenges and even undermine planned efforts to address these challenges. Therefore, integration of climate risk management into the community development efforts seems to be important and highly relevant.
Information gaps for diagnosis: Existing general information about occurrences of extreme weather events only allows for a general decision about importance of the climate risk management to the successful implementation of the project. However, as suggested by the technical report of the scoping mission, it is highly recommended that the project team undertakes the bottom-up V&A assessment in order to come up with meaningful adaptation strategy for the project. The following questions need to be addressed by the project team: 1. Perception of community about frequency and intensity of extreme weather events and climate hazards (floods, mudslides, droughts, hail etc); a. Observable changes / shifts in seasons (early / late spring rains, early snow melts, frequent floods / droughts etc); b. Occurrences of any “strange” weather phenomena during the past decade (unusually strong hail, stronger floods, longer droughts, etc); 2. Type and magnitude of losses due to these hazards (human losses, damage to infrastructure, economic losses such as crop failures etc) a. If there are changes and unusual weather patterns how these affect families (household economy) 3. Assessment of key vulnerabilities, current coping mechanisms and capacities; a. What are usual responses to such events (availability of compensations in case of damage and loss? How much resources a household spends on recovery) 4. Priority needs to address additional vulnerability induced by climatic risks and actions for risk reduction. a. What are the needs to minimize / avoid damage and loss?
Project Design: Based on the general information about the extreme weather events and climate related disasters the following adaptations can be suggested per project component (per sub-project).
36 “Climate Change Impact Assessment” UNDP/00049248
Improvement of rural infrastructure: 1. Construction of 20 private houses; 2. Construction of Getahovit-Lousadzor irrigation system operating through gravity flow and renovation of the internal irrigation network of the village 3. Reconstruction of a part of internal irrigation network in the community 4. Construction of an internal network of potable water (6 km) and renovation of two water reservoirs (150 m3) 5. Construction of a bridge over the Lousadzor River and renovation of field roads
Hazard mapping for housing: In addition to bottom-up V&A assessment it can also be suggested that the project supports a rigorous hazard mapping of the target area. This will help identify the areas of high exposure to floods and mudflows where the house constructions should be avoided. Clearly, there will be other criteria elements feeding into the decisions on house locations (e.g. proximity to arable lands, schools, accessibility to water etc.) however, disaster prone “hot- spots” should also be identified and considered in the construction scheme.
Runoff cycle and availability for water infrastructure: In construction of irrigation system that will operate through gravity flow it is important to consider flood-drought cycle of the area (based on historical data from hydrological service or SNC, community information) to identify the current and anticipated high cyclical variability that needs to be considered in the design of water infrastructure. Additionally, necessity for runoff capture and storage of flood water (as one of the means of local communal irrigation and flood management practice) needs to be identified and considered.
Bridge on the river Lusadzor: Hydrological regime of Lusadzor River needs to be considered in the bridge construction scheme. Maximum water flow volumes and flood plain expansions in last 10-20 years should be taken into account. Availability of projections for the next 30-50 years will contribute to more adequate parameters for the bridge. During past decade, it is almost a common picture in many countries of the region that bridges in rural settlements are destroyed or damaged during floods. This should be avoided by considering current and future vulnerabilities.
Development of agriculture: 1. Return of non-cultivated arable land that has turned into natural pastures back into sowing cycle; 2. Planting of persimmon orchards and distribution of seedlings to rural households.
Farm-based adaptation: Expansion to the non-cultivated arable land offers the opportunity to consider current climate variability and future risks (projections for temperature and rainfall can only be available at national level. It remains to be seen if there is a possibility to extrapolate projections for sub-national level). Based on more detailed V&A assessment, noted above, the project can provide a valuable advice and guidance to the farmers on tillage and cropping practices, on right selection of stress resistant varieties. The project can also help by creating social safety nets and pool of resources that will help withstand major climatic stresses (droughts, floods, hails and frosts). This approach will strengthen community’s coping capacity and enhance social ties within the community (identified by the project as one of the weaknesses). And also seems a viable, at least interim, option in the current circumstances where there are no insurances from natural disasters. The option of
37 “Climate Change Impact Assessment” UNDP/00049248 introducing disaster insurance scheme falls outside the scope of the project because of timeframe and a scale.
Persimmon orchards: The project already suggests an adaptation measure by introducing Persimmon orchards that will reduce the current vulnerability of fruit yield to spring frosts. However, a complete replacement of other traditional varieties may lead to other types of risks. The validity of this option may require additional expert judgment and inputs. One of the adaptation option that will allow to maintain diversity of fruit varieties and hence more resilience to market / price and climatic shocks would be installation of insulation sheets nurseries (seasonal protection infrastructure, applied in some countries with the similar threats to fruit yields from frost and hail). However, this option might be culturally unacceptable to the Lusadzor community (can be investigated).
Adaptive capacity development: As one of the community development activities the project should introduce a set of actions for adaptive capacity development. Stemming from the peculiarities of the household economy in the target area adaptive capacity development may focus on improving knowledge and understanding of climatic stressors to agricultural production in immediate, short and longer term perspective. This can be done by providing climate information and warning system to the communities, so that they have sufficient time and information to adequately respond and adapt. 7. Improving local seasonal forecasting (e.g. flood early warning, weather forecasting, etc.); Dissemination through a free newspaper - ‘Farmer”; 8. Introducing farm piloting / demonstration for adaptation measures (e.g. Intercropping, tillage practice, new varieties, flood water management, testing seasonal protection infrastructure against hail and frost, etc); 9. Community meetings and farmer trainings on climate risk management to identify and help develop farmers coping and adaptation strategies (training will focus on how farmers are already responding to the risks, what works well and what not and why, what are the primary needs to improve adaptive capacity and minimize damage and losses); 10. Creation of communal social nets and common pool of resources (e.g. common pool of seeds and seedlings, communal irrigation management, etc) to help cope with major stressors in the ; 11. Facilitate culturally acceptable alternative livelihoods, such as carpet making, to create income buffers as means to adaptation.
Policy impact: 1. Incorporation of adaptation measures into the three year integrated development plans for communities; 2. Addressing adaptation costs in local budgets to develop local adaptive capacity (introduction of local stress resistant varieties, local water and flood management, etc);
Project Implementation: Prior to making adjustments to the project, as to introduce climate risk management system, it is important that the suggested adaptations are analyzed against the following criteria elements. This can be done by the project experts or with participation of local communities.
38 “Climate Change Impact Assessment” UNDP/00049248
Criteria for Analyzing Adaptation:
Adaptation measure
13 10
14 15
12
11 Technical Feasibility Cost effectiveness Adequacy for Current Climate Social / Cultural Feasibility Consistency With Project Hazard mapping for housing Integration of current hydrological variability and projections into the design of rural irrigation scheme. Runoff capture and flood water storage facility (e.g. communal reservoir) as flood management and communal irrigation infrastructure Integration of water flow variability and projections for bridge construction on Lusadzor river Introducing persimmon as stress resistant variety for improving productivity of orchids Introducing insulation sheets nurseries as protection against hail and frost as means to maintain diversity of orchid yields and market security Organising pilot farms for demonstrating benefits of adaptation measures (intercropping, insulation, tillage, flood water management etc) Improving local weather forecasting and early warning. Series of workshops for farmers on climate risks to agricultural productivities and options to adapt (resulting in farmer coping and adaptation strategies) Creation of communal safety nets and common resource pools (seeds, communal irrigation) as means for coping and adaptation
10 Effectiveness as a solution to problems arising from climate variability and change (benefits, damage avoided or mitigated, losses and associated costs avoided or minimized) 11 Additional cost of adaptation measure compared to cost of not modifying the project 12 Benefits in under the current climatic conditions and future scenarios. 13 In line with the project purpose, scope and a timeframe; supports and reinforces the core objectives of the project and does not diverts its focus to other aims. 14 Capacity and means to implement the measure. 15 The proposed measure does not disrupt social and cultural values and traditions, but rather builds on them
39 “Climate Change Impact Assessment” UNDP/00049248 Adaptation measure
13 10
14 15
12
11 Technical Feasibility Cost effectiveness Adequacy for Current Climate Social / Cultural Feasibility Consistency With Project Promoting culturally acceptable alternative livelihoods, such as carpet making, to create income buffers for adaptation Incorporation of adaptation measures into the integrated development plans for communities Local financing of adaptation measures
References:
Red Cross / Red Crescent Centre on climate change and disaster preparedness (2007), Climate Guide;
USAID (2007), Adapting to Climate Variability and Change: a guidance manual for development planning;
UNDP (2005) Adaptation Policy Framework for Climate Change: Developing strategies, policies and measures;
AIACC Working paper #39 (2006), Local Perspectives on Adaptation to Climate Change: Lessons from Mexico and Argentina;
GECAFS Working paper #3 (2006) Assessing the vulnerability of food systems to global environmental change: a conceptual and methodological review;
Project concept: Improvement of crop production systems to reduce food insecurity in the lowlands of Lesotho.
40 “Climate Change Impact Assessment” UNDP/00049248 Annex 3. Assessment of the pilot climate change impact assessment of the Lusadzor community project in Armenia
Background UNDP CO Armenia with support of UNDP Bratislava Regional Centre has targeted the “Integrated Development Plan for Lusadzor Community Project” as a pilot for integrating climate risk management. Lusadzor is located in the Tavush region (marz) which is considered as one of the most disaster prone regions of Armenia. General impacts of projected climate changes in Armenia include: extreme weather events and disasters and changes in rainfall and temperatures patterns. In particular, floods, hails, frosts and mudflows constitute main climatic hazards in Tavush Marz.
A climate change impact assessment (climate proofing exercise) was carried out in 2008 with the aim to identify climate risks and to climate proof the UNDP project activities. Although it is recognised that extreme weather events and climate hazards, especially mudflows and hails, pose important risks to local development schemes in Tavush Marz, no targeted efforts have been made to address these risks in local planning and development activities. The climate change impact assessment is thus an important step in the climate risk management of the community.
Prior to the climate change impact assessment, a number of support documents have been elaborated which diagnose main climate risks and hazards in Lusadzor. These include: a) Screening note of Lusadzor community with climate change problem diagnosis; b) Technical report (September 2007) on Armenia’s climate change profile (key risks and vulnerabilities); UNDP programme priorities; Armenian national priorities for adaptation and UNCT programmes in Armenia.
Furthermore, the project document for “Lousadzor Integrated Development Plan” has been consulted for a general description of Lusadzor.
The aim of the present report is to review the climate change impact assessment process, the methodology used and the outcomes of the climate change impact assessment and is based on: a) Review of the climate change impact assessment; the Lousadzor Community Development Plan, the Screening note and the Technical Report; b) Interviews with local experts, the Armenian CO and other relevant stakeholders during a country visit from 17 – 21 November, 2008.
Climate proofing process The purpose of the climate change impact assessment is to identify climate risks and to climate proof the UNDP project activities. The exercise is based on a thorough analysis of available climate data (both from official data and through interviews) and a lot of effort has equally gone into developing and defining the methodology for how to climate proof.
The climate change impact assessment thus: a) analyses current and projected climate change impacts on Lusadzor community, b) identifies impacts likely to affect project activities; c) selects a number of a priority adaptation options to be implemented by UNDP, local authorities and donors and d) costs the adaptation measures. The assessment report is structured around 5 major chapters: 1. Background information, including location, socio-economic data, agriculture and climate and landscape;
41 “Climate Change Impact Assessment” UNDP/00049248 2. Main findings of the climate data analysis, including methodology, analysis of interviews and available temperature and rainfall data; 3. Screening of the Lusadzor community development project on its climate change sensitivity; 4. Identification and assessment of adaptation measures, including identification and costing of adaptation measure and status of implementation of proposed adaptation measures; 5. Conclusions, including lessons learned
Background information Background information includes the description of the geographical location of Lusadzor; population data, the agricultural production (except livestock production), and data on rainfall, temperature, soil types and topography. For an outside reader without prior knowledge of the region the description of the project area is insufficient to get an overview of the project setting and potential climate risks in general. It might have been useful to include: - A description of major infrastructure, such as roads, bridges, irrigation dams and channels and gas lines. Although the rehabilitation of this infrastructure may not be a project activity in itself, the lack of infrastructure or if the infrastructure is at risk, it may jeopardize project activities. This information will help give an overview of the accessibility and remoteness of the community and are important in the assessment of how vulnerable the community is to disasters. This information can be based on a visual assessment of the area. - Land-use. Although agriculture account for the vast majority of the surface, it might be useful to specify other uses, such as forest areas, which is quite important in Lusadzor. Both timber and non-timber forest products are important as a supplement to other incomes, especially as a mean to reduce the dependence on agriculture. - Energy sources and consumption. Prior to the project, the community depended to a large extent of wood for cooking and heating. The gasification of Lusadzor is likely to reduce the consumption and dependence on fire wood, which will have a positive impact on CO2 emissions and forest resources, which again may reduce the risk of erosion and hence mudflows. - Food security situation. A question could be included in the interview survey regarding the level of subsistence farming and food security. Do farmers commercialise all their production and is food security an issue in Lusadzor? This is an important indicator for how vulnerable the population is to climate variations and changes and thus whether they can able to cope.
Main findings of the climate data analysis The main findings are divided into the three sub-chapters; a) methodology, b) main findings of the interviews and c) an analysis of air temperature and precipitation data. The inclusion of all three subchapters in one is a bit confusing. All sub-chapters are very important and need to be separate chapters. A specific chapter on methodology and approach would be relevant to the whole climate change impact assessment – not just the data analysis part, but indeed also the identification and ranking of adaptation measures. - Methodology – see chapter 3. - Main findings of the interviews. The results of the interviews indicate an increase in the frequency and intensity of climatic hazards - especially flooding, mudflows, hail, strong winds, frost and less available water. Although there is no specific data for Lusadzor, general data for Tavush Marz backs this perception.
42 “Climate Change Impact Assessment” UNDP/00049248 The chapter also includes an assessment of economic losses due to climatic hazards, e.g. damage to infrastructure and failed harvests. A table of economic losses due to climatic events has been created based on official data from the Tavush agricultural department. As this data is not based on interviews, it should be in a separate chapter. According to the table a considerable proportion of the annual harvest is damaged every year as a result of climatic events. E.g. according to table 100% of the vegetables production was destroyed in 2006. Economic losses due to climatic events are always difficult to estimate, but it is unlikely that 100% of the vegetables production, 70% of all fruits and beans and 90% of the total corn production was destroyed in one year. This is further evident by the fact the 100% of the harvest that was lost to flooding in 2007 on only 0.1 ha of land. Such data need to be used with prudence and this table cannot stand alone, but needs an assessment of the reliability and explanation. - Analysis of air temperature and precipitation data. The thorough analysis of climatic data shows a general decrease in annual average atmospheric precipitation and an increase in average annual temperature in the last 10-15 years. The annual data hide considerable inter-annual variations. Especially the decrease in rainfall during the summer season is a major problem and may deserve a bit more elaboration.
Screening of community development projects on their climate change sensitivity In this chapter Project activities are screened in regards to their sensitivity to climate changes and the value of the activity as an adaptation measure is assessed. The main activities are related to the improvement of infrastructures, energy conservation and development of agriculture practices, such as irrigation, diversification (livestock, orchards) and water conservation. It is estimated that most of the activities are positive and will reduce the vulnerability to climate changes and no particular climate change related risks have been identified.
The Screening Note included a very thorough and detailed analysis of Project activities, and their sensitivity to climate changes - both positive and negative - and it would have been useful to include this analysis in the climate change impact assessment.
Identification and assessment of adaptation measures The climate change impact assessment has identified a number of adaptation measures related to the project activities, which may help make the Lusadzor community less vulnerable to climate variabilities and changes. Many of these adaptation measures are identified in the Screening Note, which contains a very thorough and detailed analysis of the proposed activities, their sensitivity to climate changes, and analysis of possible adaptation measures. It would have been useful to include this analysis in the climate change impact assessment.
All measures are considered highly relevant. Comments are as follows: (i) Transformation of non-cultivated lands to arable lands. The sentence “Transformation of non-cultivated lands to arable lands offers the opportunity to consider current climate variability and future risks” is unclear and should be explained. It is true that the introduction of irrigation - in conjunction with modern water saving technologies - will reduce the dependency on rainfall, but may entail other undesired impacts which need to be addressed, such as unequal access to the irrigation areas, negative impacts on the environmental (erosion, increased use of chemicals, reduced biodiversity, etc.). (ii) Improving local seasonal forecasting and early warning. This is extremely important. However, there are several steps which need to be in place to make this useful. This
43 “Climate Change Impact Assessment” UNDP/00049248 includes a) availability of reliable data; b) dissemination of data; and c) knowledge of what to do with the information. A discussion on this would have been useful. (iii) Facilitation of community meetings and farmer trainings on climate risk management to identify and help develop farmers’ coping and adaptation strategies. This is equally very useful and should include a study of existing coping strategies. (iv) Creation of communal social nets and common pool of resources to help cope with major stressors in the community. As above, the dissemination of strategies how to adapt as well as financing options and possibilities need to be discussed and incorporated. (v) Consideration of climatic hazards in infrastructure development works as part of community development plans. Infrastructure to be climate proofed includes water & sanitation, transport and constructions infrastructure. A weak point is often the inadequate design of infrastructure and investment in infrastructure with a longer life- span should be considered with the expected future climate in mind, e.g. options for water storage, dimension of dams, irrigations schemes, culverts and bridges in road building and location of public building, such as health clinics and schools. This is quite well explained in the Screening Note, and it would have been useful to include it in the climate change impact assessment as well.
Conclusion The report concludes that it has been recognized that the Integrated Development Plan for Lusadzor Community Project is not “climate-risk neutral” as it has the potential, through its local development planning and pilot actions, to either increase or diminish vulnerability to climate change impacts. However, no risks or negative impacts have been presented. The report needs to address e.g. risks (pest, lack of diversification) related to the introduction of a sole tree crop such as persimmon, or e.g. erosion problem on cultivated land if permanent vegetation is removed, etc.
The report further concludes: The study being limited in its scope and scale however, a proof of viability of methodology applied in the current study can be useful for climate proofing of the community development / area-based development projects and rural development planning. The climate screening/proofing methodology is well developed and follows the approach established in various donor guidelines. However, this methodology is not explained in the report and it was necessary to read several support documents to both get an idea of project activities and the approach.
The report formulates a number lessons learned, which is very useful. Comments to lessons learned are: - Lessons learned no. 1 “The climate change impact assessment was conducted in three stages: analysis of climate-related data; identification of climate risk prone project activities; selection of adaptation measures” cannot be considered a lesson learned. It is a description of the methodology. - Lessons learned no. 3 “In the process of project screening it was concluded that some of the project activities have inherent adaptation value, which was important to identify before recommending any further adaptation measures”. The identification of existing (or proposed) adaptation strategies is a very important element in the climate change impact assessment and should be part of the screening methodology.
The assessment report concludes that two of the proposed adaptation measures are being implemented by MCA Armenia and UNDP; however there is no mention of time frame and actions to ensure the funding of other measures.
44 “Climate Change Impact Assessment” UNDP/00049248 Methodology Climate proofing and mainstreaming climate change into development programmes is a relatively new domain and at present very few methodologies or guidelines have been developed. Climate screening and proofing exercises will inevitably vary according to what is being screened/proofed, e.g. is it a community of a region/country, or a local level project, or a national programme, etc. However, the basic steps in a climate-proofing exercise are: a) estimation of expected climate changes, b) assessment of the sensitivity of proposed activities to the expected climate changes, c) assessment of the implicated population vulnerability to - and their possibility to cope with - these changes, and d) identification of mitigation and adaptation measures.
The methodology in the Lusadzor climate change impacts assessment report describes the data collection process and not the methodology on how to do a climate change impact assessment as such (V&A analysis, adaptation measures selection criteria, etc.).
The Screening Note and the Technical Report on the other hand provide a very detailed description of this methodology and approach, as well as a thorough description and analysis of risks and vulnerabilities and this methodology is in line with other climate-proofing exercises. Although, the climate change impact assessment bases its assessment on this methodology, it is not described in the report and it would have been useful to include this - or parts of this - in the report.
The data collection methodology is based on: 1. Interviews with local community (local residents and the rural community administration) and representatives of Department of Agriculture of Tavush Marz. There is mention of interviews with 12 residents, meetings with representatives of the Department of Agriculture in Tavush Marz; however there is no mention of consultations with the local community. This consultation process should be described in the methodology. Questionnaires (and not consultations, as it states) were used to retrieve information on a number of issues. Other issues which could have been included in the questionnaire are: - Awareness and knowledge of climate changes - Use of new / traditional farming practices - Commercialisation or subsistence farming - Food security
A number of adaptation activities have been formulated in consultations with the local community. These consultation meetings are not described in the methodology. The reports mention: “Two field trips were organized to Lusadzor community in order to conduct survey of the local residents in line with the above-mentioned questionnaire items. In total, 12 residents were interviewed, including the rural community leader”. It is not clear how these surveys were conducted and whether only 12 members of the community were consulted.
2. Analysis of economic losses due to climatic events. This information was obtained in the Department of Agriculture of Tavush Marz. As mentioned earlier, economic losses due to climatic events are always difficult to estimate, and this data is based on what was reported in order to obtain compensation. In my opinion, it is not reliable data and should not have been included without explanation and analyses. As it stands now, it raises more questions than it answers and gives a wrong impression of the gravity of the hazards. It can only be used as an indication of types and magnitude of hazards.
45 “Climate Change Impact Assessment” UNDP/00049248
3. Analysis of meteorological data. The collection of meteorological data falls under the Armenian State Hydro-meteorological Service. However, as they do not have meteorological stations in Lusadzor community, the analysis is based on data from two meteorological posts in nearby communities, displaying similar conditions to Lusadzor. The lack of data is common problem to many developing countries and makes it difficult to assess exact climatic trends. The use of available data from nearby locations and down scaling of general trends, are often the only possibilities and can be used to indicate trends rather than exact data. The use of data from nearby locations is generally recognised as the most reliable solution and more reliable than down scaling.
Outcomes of the pilot climate change impact assessment Available data has been thoroughly analysed. Although climate change forecasts are difficult to make without much data, a good attempt has been made.
Cost-effectiveness. There is no vulnerability assessment or an assessment of food security and subsistence issues, health issues, etc. in the climate change impact assessment report. An elaborated vulnerability assessment and climate proofing exercise can be quite costly; however, an elaborated vulnerability assessment is not necessary to get an overview of the sensibility of the proposed activities for a small community project as Lusadzor. If the projected climate changes and the proposed activities are known it is possible to quickly get an overview of the sensitivity of the proposed activities and subsequently identify adaptation measures. These questions could have been included in the questionnaire without much extra effort. For instance, consultation meetings/group interviews could have been organised, where more people were consulted.
The Lusadzor is a first climate proofing exercise and therefore a bit of a test, and a lot of time and effort was spent on the development of methodology and formulation of which questions to ask. This should not be necessary next time
Funding of adaptation measure. The issue of additional funds for climate change adaptation has to be highlighted. The report estimates the costs of the proposed adaptation measures. Often these measures are “an investment” and makes good sense, however if funds are not available they are easily ignored and not prioritised. Although many proposals for supporting adaptation measures entail “doing development better”16, significant additional funds will be required to upgrade infrastructure, to ensure agricultural productivity gains, to introduce better early warning systems, to protect vulnerable communities and so on. Funding has only been secured for two of the six proposed adaptation measures. Based on which criteria were these two measures selected?
Disaster risk reduction. An issue that is not dealt with in the report is disaster risk reduction measures and disaster preparedness. The links between climate change adaptation and disaster risk reduction is increasingly apparent and the negative implications on food security and livelihoods are high, especially in an area like Lusadzor where a large part of the population depends on agriculture. Changing climate patterns thus increase the urgency to invest in disaster risk reduction, disaster management and preparedness.
16 In the sense implied by the 2006 Stern report on the economics of climate change: that adaptation will be an extension of “good development practice.”
46 “Climate Change Impact Assessment” UNDP/00049248 Annex 4. Location of Lusadzor Community
47 “Climate Change Impact Assessment” UNDP/00049248 Annex 5. Main Climate Features of Lusadzor
48 “Climate Change Impact Assessment” UNDP/00049248
Annex 6. Questionnaire for Field Survey
I. GENERAL INFORMATION
Name: ______
Age: ______
Number of people in the household: ______
Landholding size: ______
Agriculture practices: ______
______
______
Main source of income: ______
II. CLIMATIC HAZARDS a. Floods a1. Frequency of floods in the period 2000-2007 □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A a2. Intensity of Floods □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A a3. Type of loss □ Human losses □ Damage to infrastructure □ Economic losses □ N/A □ Other ______a4. Magnitude of loss ______AMD for the period ______□ Difficult to answer □ N/A a5. Total by years: 2000 ______AMD; 2001 ______AMD; 2002 ______AMD; 2003 ______AMD 2004 ______AMD; 2005 ______AMD; 2006 ______AMD; 2007 ______AMD a6. Verification of source ______
b. Mudflows b1. Frequency of mudflows in the period 2000-2007 □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A b2. Intensity of mudflows □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A
49 “Climate Change Impact Assessment” UNDP/00049248 b3. Type of loss □ Human losses □ Damage to infrastructure □ Economic losses □ N/A □ Other ______b4. Magnitude of loss ______AMD for the period ______□ Difficult to answer □ N/A b5. Total by years: 2000 ______AMD; 2001 ______AMD; 2002 ______AMD; 2003 ______AMD 2004 ______AMD; 2005 ______AMD; 2006 ______AMD; 2007 ______AMD b6. Verification of source ______
c. Landslides c1. Frequency of landslides in the period 2000-2007 □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A c2. Intensity of landslides □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A c3. Type of loss □ Human losses □ Damage to infrastructure □ Economic losses □ N/A □ Other ______c4. Magnitude of loss ______AMD for the period ______□ Difficult to answer □ N/A c5. Total by years: 2000 ______AMD; 2001 ______AMD; 2002 ______AMD; 2003 ______AMD 2004 ______AMD; 2005 ______AMD; 2006 ______AMD; 2007 ______AMD c6. Verification of source ______
d. Droughts d1. Frequency of droughts in the period 2000-2007 □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A d2. Intensity of droughts □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A d3. Type of loss □ Human losses □ Damage to infrastructure □ Economic losses □ N/A □ Other ______d4. Magnitude of loss ______AMD for the period ______□ Difficult to answer □ N/A d5. Total by years: 2000 ______AMD; 2001 ______AMD; 2002 ______AMD; 2003 ______AMD 2004 ______AMD; 2005 ______AMD; 2006 ______AMD; 2007 ______AMD d6. Verification of source ______
50 “Climate Change Impact Assessment” UNDP/00049248 e. Hail e1. Frequency of hails in the period 2000-2007 □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A e2. Intensity of Hails □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A e3. Type of loss □ Human losses □ Damage to infrastructure □ Economic losses □ N/A □ Other ______e4. Magnitude of loss ______AMD for the period ______□ Difficult to answer □ N/A e5. Total by years: 2000 ______AMD; 2001 ______AMD; 2002 ______AMD; 2003 ______AMD 2004 ______AMD; 2005 ______AMD; 2006 ______AMD; 2007 ______AMD e6. Verification of source ______
f. Wind f1. Frequency of winds in the period 2000-2007 □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A f2. Intensity of Floods □ Increased □ Remained the same □ Decreased □ Difficult to answer □ N/A f3. Type of loss □ Human losses □ Damage to infrastructure □ Economic losses □ N/A □ Other ______f4. Magnitude of loss ______AMD for the period ______□ Difficult to answer □ N/A f5. Total by years: 2000 ______AMD; 2001 ______AMD; 2002 ______AMD; 2003 ______AMD 2004 ______AMD; 2005 ______AMD; 2006 ______AMD; 2007 ______AMD f6. Verification of source ______
51 “Climate Change Impact Assessment” UNDP/00049248 III. COPING WITH CLIMATE EXTREMES
1. Access to climate information □ Accessible (please provide source): ______□ Limited Access □ Not Accessible □ Difficult to answer
2. Use of climate information □ Used □ Used to some extent □ Not used □ N/A
3. Is your farm sensitive to climate impacts? □ Yes □ No □ Difficult to answer
4. Type of adjustments you make to cope with climate risks, including indigenous knowledge: ____
______
______
______
5. Obstacles you are facing while coping with climate risks □ Lack of alternatives crops □ No information on how to cope with climate risks □ Lack of finances □ Other (please describe) ______
______
6. Which of these adaptation measures you think will help in cope with climate risks? □ Crop switching □ Access to alternative non-farm income sources □ Availability and use of climate information □ Changes in tillage practices □ Water conservation □ Drop irrigation □ Availability of compensatory mechanisms in case of crop loss and other support financing Other (please describe) ______
______
______
______
7. Please provide additional comments on climatic hazards as appropriate: ______
______
______
______
52 “Climate Change Impact Assessment” UNDP/00049248 Annex 7. Meteorological Stations in Tavush Marz
Annex 8. Average Monthly Temperature, Ijevan
Average Monthly Air Temperature Recorded in Ijevan Meteorological Post, in 0C
Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec AVE 1913 - - - - - 16,9 22,9 23,1 18,8 10,5 6,8 5,7 1914 2,4 3,2 9,5 8,7 13,7 16,6 21,5 20,9 16,1 11,1 3,9 1,5 10,76 1915 7,9 4,1 6,7 8,6 13,1 16,8 20,1 20,2 17,1 12,2 7,9 6,1 11,73 1916 1,5 0,8 5,5 10,8 15,9 17,9 21,9 20,9 16,2 11,2 6,5 3,6 11,06 1917 3,8 0 8,2 14,7 13,3 17,4 22,8 21,1 17,9 11,4 8,8 - 1925 ------11,5 8,2 5,5 1926 - 0,6 5,2 9,7 15,1 - 19,9 21,7 15,3 12,9 - 3,7 1927 -0,8 - 6,2 9,6 14 - 21,2 22,2 18,7 15,6 7,2 1,1 1928 -1,0 -1,2 0,3 11,5 14,3 17,1 19,9 - 17,7 10,5 8,8 2,6 1929 -1,5 -3,3 0,7 9,7 16,3 17,1 20,5 23,2 17,1 12,5 7,0 -0,7 9,88 1930 -0,8 - 6,1 9,4 15,0 16,6 22,2 23,0 17,0 11,4 7,9 2,0 1931 -1,3 -1,5 6,9 7,9 13,0 18,1 21,7 20,7 19,0 11,9 3,6 0,7 10,06 1932 -0,8 -0,2 4,6 11,2 14,8 19,7 19,9 21,5 16,1 14,2 4,5 -0,4 10,43 1933 -2,4 1,7 3,0 8,3 15,1 16,3 20,3 22,3 18,3 12,0 8,2 0,7 10,32 1934 -3,1 1,7 5,8 7,8 14,2 17,8 21,3 20,1 16,0 11,9 5,9 0,6 10,00 1935 -3,8 2,6 2,9 11,4 14,5 17,0 21,6 22,5 19,3 13,6 3,9 3,0 10,71 1936 0,7 0,9 4,3 10,8 13,3 18,0 21,6 21,2 16,1 11,8 7,4 1,8 10,66 1937 -2,5 2,2 4,8 9,2 13,5 15,9 22,4 22,3 20,1 13,4 7,9 4,9 11,18 1938 1,7 1,2 2,5 11,2 14,7 18,0 22,2 21,5 17,5 12,4 6,0 2,1 10,92 1939 1,6 1,5 3,3 9,6 17,0 19,8 20,8 19,7 16,3 11,9 5,3 3,7 10,88 1940 -0,9 1,8 4,0 12,2 13,1 18,5 21,6 23,3 17,9 11,7 7,7 5,7 11,38 1941 0,9 5,8 5,2 11,2 16,1 20,3 22,7 20,1 18,8 10,3 3,5 0,9 11,32 1942 -0,1 0,2 2,7 8,7 15,0 19,9 21,8 21,8 16,9 12,3 6,5 2,0 10,64 1943 -1,0 -1,2 0,5 10,8 15,2 18,0 20,5 21,5 16,9 12,9 6,7 3,1 10,33 1944 0,7 5,0 8,7 9,8 14,5 18,9 21,3 19,3 17,6 12,3 7,1 -1,6 11,13 1945 0,3 -1,3 3,5 9,0 14,4 16,9 21,4 21,8 17,7 11,5 5,1 1,9 10,18 1946 -0,3 4,0 5,1 9,6 15,4 17,7 19,9 20,1 18,7 9,5 6,5 -1,0 10,43 1947 -0,3 2,5 9,0 10,5 13,7 18,8 21,8 20,2 16,0 10,6 8,7 4,6 11,34 1948 5,5 3,6 2,0 7,6 15,5 20,0 22,4 22,4 15,9 10,6 4,9 -1,5 10,74 1949 -1,9 -1,4 3,4 8,4 14,7 20,2 23,4 20,1 15,1 9,2 6,9 1,4 9,96 1950 -4,1 -0,2 5,5 13,2 16,3 17,2 20,8 20,5 18,6 11,1 5,0 2,9 10,57 1951 0,8 -0,4 7,5 12,9 15,0 18,2 22,2 22,3 16,6 8,1 6,3 1,5 10,92 1952 1,8 1,9 4,5 8,5 14,5 16,6 21,8 22,5 17,7 15,0 5,1 4,0 11,16 1953 3,1 3,7 2,7 9,9 15,4 18,8 20,7 21,8 16,5 11,3 1,7 -1,6 10,33 1954 -1,3 -1,8 2,4 7,7 15,2 18,3 22,2 22,4 17,5 13,6 7,5 4,7 10,70 1955 3,2 5,7 4,5 9,3 14,8 18,8 21,3 20,6 16,9 13,7 7,4 2,9 11,59 1956 2,6 -1,2 1,5 10,9 12,1 17,8 20,0 21,4 14,5 10,5 5,0 -0,2 9,58 1957 -1,7 4,1 4,7 11,2 15,7 18,3 20,9 22,6 20,0 12,0 6,1 4,5 11,53 1958 3,5 5,0 5,6 9,5 16,5 18,7 20,4 21,4 16,2 10,5 3,5 4,1 11,24 1959 4,2 -2,1 2,4 11,8 14,2 16,7 21,3 20,3 14,7 8,7 4,8 2,8 9,98 1960 3,8 3,3 2,0 8,6 14,8 18,0 20,2 20,0 17,4 13,5 8,1 3,9 11,13 1961 0,3 1,4 4,9 11,7 18,0 20,4 21,1 22,0 15,9 10,6 8,1 5,5 11,66 1962 2,6 3,5 8,5 9,4 15,4 18,9 23,9 21,6 18,1 12,0 7,0 6,1 12,25 1963 4,5 3,9 4,3 9,8 13,4 17,1 20,6 19,7 17,2 12,7 6,7 4,1 11,17 1964 -2,2 1,6 5,2 9,3 14,9 19,5 20,3 19,8 17,2 11,7 7,2 1,9 10,53 1965 -0,2 1,9 5,4 8,2 15,3 18,4 20,8 21,1 16,9 9,2 8,1 6,3 10,95 1966 6,4 6,6 6,8 11,8 13,8 18,5 23,6 23,6 16,6 13,2 9,4 4,8 12,93 1967 3,2 -1,3 4,2 9,1 15,1 17,2 19,8 21,2 16,0 13,0 6,9 4,0 10,70
54 Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec AVE 1968 1,8 2,4 4,4 10,4 16,7 16,9 21,4 20,3 18,7 12,5 8,5 2,1 11,34 1969 -3,3 -3,0 2,7 8,3 15,2 20,2 19,9 21,1 16,4 10,4 7,3 5,2 10,03 1970 2,3 4,6 6,2 13,0 15,6 18,5 22,9 20,2 16,8 10,3 8,2 1,6 11,68 1971 1,6 1,3 6,7 9,1 16,0 17,7 24,2 21,2 20,1 11,1 8,5 2,6 11,68 1972 -4,9 -2,6 3,0 13,5 14,3 18,7 22,4 21,9 17,6 14,6 7,4 -0,7 10,43 1973 -1,8 5,9 5,1 10,4 14,8 17,1 20,8 21,4 15,9 12,9 5,8 2,8 10,93 1974 -1,8 0,4 4,1 7,6 15,2 19,0 20,7 19,3 15,3 14,9 7,8 3,7 10,52 1975 1,1 0,0 5,3 13,1 15,3 20,6 23,7 22,0 17,0 10,6 5,7 2,6 11,42 1976 2,2 -4,1 2,4 11,2 13,7 17,4 20,4 22,4 16,6 10,2 6,1 3,6 10,18 1977 -3,9 5,5 5,8 12,0 15,1 18,8 21,2 21,9 16,7 8,4 7,6 1,0 10,84 1978 1,3 1,4 7,0 9,1 13,7 16,3 22,4 20,3 18,9 12,4 4,2 4,9 10,99 1979 1,1 3,9 7,1 10,7 14,9 17,3 21,4 23,4 19,6 12,0 7,5 3,4 11,86 1980 -0,8 -0,1 4,3 10,6 15,5 20,0 23,8 20,5 16,9 10,5 8,5 5,6 11,28 1981 3,6 3,8 6,1 8,6 13,0 18,7 22,4 20,4 18,3 13,7 7,1 6,3 11,83 1982 0,8 0,1 3,4 12,8 15,0 17,8 21,0 19,8 17,0 10,8 4,2 3,7 10,53 1983 -1,1 4,3 4,6 12,1 15,2 17,5 22,4 19,9 16,0 10,9 7,2 3,3 11,03 1984 2,5 -3,7 4,4 10,5 13,8 18,4 23,0 20,4 18,7 11,7 6,4 -0,9 10,43 1985 3,0 0,6 2,8 12,0 16,4 19,9 19,9 22,3 18,3 10,4 8,8 2,8 11,43 1986 4,0 1,5 2,7 11,9 12,6 18,1 22,1 22,5 19,7 11,7 5,1 3,0 11,24 1987 3,9 3,2 2,7 8,8 16,6 20,3 23,0 21,1 15,9 8,4 5,9 3,8 11,13 1988 1,1 0,6 4,9 11,3 13,0 18,5 21,6 19,6 16,3 11,6 6,0 4,3 10,73 1989 0,2 0,7 7,9 13,9 15,8 19,9 23,1 23,0 17,8 11,5 7,5 2,6 11,99 1990 0,1 2,5 6,8 9,8 13,2 19,1 22,5 20,3 18,6 12,1 9,4 3,3 11,48 1991 1,0 0,4 4,9 11,8 13,9 19,3 22,9 22,2 17,8 14,3 6,3 3,0 11,48 1992 -0,5 -0,2 4,7 9,5 13,4 17,4 20,6 20,5 16,2 11,7 6,5 1,2 10,08 1993 -0,3 -1,4 5,8 9,8 14,3 18,5 21,8 21,4 17,5 11,0 1,7 3,7 10,32 1994 4,2 -0,9 6,3 11,7 14,9 17,8 20,8 20,5 18,5 13,1 6,9 0,0 11,15 1995 3,9 4,5 6,9 11,7 16,6 20,1 22,0 22,6 18,4 10,8 8,6 3,3 12,45 1996 -0,3 2,6 3,6 8,7 16,3 18,3 23,5 22,8 18,3 12,2 6,4 5,6 11,50 1997 2,4 1,0 3,6 11,2 16,5 20,0 22,5 23,0 15,3 15,0 6,4 4,1 11,75 1998 0,9 1,7 5,6 14,3 15,8 22,3 23,3 24,0 18,1 13,9 8,3 5,1 12,78 1999 4,3 5,3 6,3 10,6 13,6 19,0 22,4 24,7 17,3 12,8 6,0 5,9 12,35 2000 -0,1 3,7 5,1 14,1 14,2 20,0 25,3 24,5 18,8 11,1 6,0 5,0 12,31 2001 2,4 5,3 9,2 11,9 14,1 20,5 23,4 23,8 18,7 11,7 8,3 3,9 12,77 2002 1,1 5,8 8,5 9,2 13,4 18,5 22,6 20,8 19,5 14,1 8,9 -1,3 11,76 2003 3,9 2,1 2,3 8,0 16,0 17,8 21,4 22,7 17,4 14,3 7,4 4,6 11,49 2004 5,1 5,9 8,9 10,6 15,1 19,4 21,5 22,8 17,8 13,0 8,3 2,9 12,61 2005 2,0 1,4 5,5 11,5 15,4 18,1 23,4 22,7 17,9 11,6 7,5 4,7 11,81 2006 -1,3 3,7 7,2 11,1 14,9 22,5 21,4 26,0 18,6 14,0 6,2 1,1 12,12
55 Annex 9. Average Monthly Precipitation, Ijevan
Average Monthly Atmospheric Precipitation Recorded in Ijevan Meteorological Post, in mm
Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec TOTAL 1935 43,4 28,7 47,6 31,7 37,8 109,7 35,3 43,3 11,2 49,8 55,3 16,8 511 1936 3,5 23,8 43,1 74,5 191,6 120,9 117,3 5,4 57,4 63,9 32,1 4,2 738 1937 14,3 4,6 46,3 82 142,1 154,6 33,8 56,2 10,3 22,3 12,6 18,2 597 1938 13,1 62,1 18,1 71 139,6 31 60,7 40,6 93 18,6 6,6 32,1 587 1939 5,7 19,8 39,4 43,3 30,6 83,2 95,2 92,2 19,9 64,1 34,9 11,6 540 1940 45,2 37,7 34 53 91,2 137,8 85,3 3 37,6 73,7 12,5 28,2 639 1941 30,8 25,4 60,5 61,9 86,6 23,3 33,4 68,1 47 20,8 65,5 22,4 546 1942 43,3 18,4 47,3 47 155,8 48,9 55,8 14,8 49,4 91,5 61,9 19,9 654 1943 14,1 28,9 48 2,7 90,5 78,5 96 41,2 28,6 47,6 35,4 18,7 530 1944 25,2 4,4 23,5 53,7 110,3 100,2 95,1 59 27,6 45,8 30,1 18,9 594 1945 11,1 24,6 50 55,8 98,2 91,6 37,6 21,5 21,8 33,9 38,1 22 506 1946 29,7 29,6 35 33,2 98,4 115,1 57,1 43 29,5 117,4 13,2 34,8 636 1947 32,1 5,7 32,8 98,2 105,9 78,2 66,1 43,4 89,2 35,1 53,9 12,6 653 1948 23 33,8 83,7 74,8 63,9 88,2 5 25,7 29,1 76,7 44,8 26 575 1949 10,5 19,7 44,1 31,5 132,9 41,6 9,1 93,1 71,2 33,5 11,3 10,5 509 1950 38,5 17,1 40,1 15,4 87,4 93,1 4,7 27,5 1 73,8 35,8 9,8 444 1951 46,9 19,3 4,7 45,9 94,5 124,6 54,5 20,9 87,3 128,5 19 24,3 670 1952 3,7 34 36,3 46,4 61,8 207,5 0 4,8 12,5 4,5 27 14,5 453 1953 6,3 22,7 40,8 55,2 57,5 99,6 60,5 43,5 8,5 26,2 89,3 21,1 531 1954 22,5 31,7 51,4 52,6 97 80,4 88,5 29 47,9 22,3 30,2 17 571 1955 8,2 5,4 69,3 60 133,1 71,5 40,3 56,8 67 13,5 50,4 16,9 592 1956 9 52,2 69,1 59,1 148,4 43,2 43,7 9,6 72,1 16,8 33,5 36,4 593 1957 28,9 8,7 49,1 13 84 46,9 84,4 20 39,9 2,9 12,2 3,1 393 1958 9,5 11 56,7 70,2 44,4 112,1 61,6 27,9 38,9 34 21,4 23 511 1959 17 22,1 66,4 25,5 143,8 134,8 32,4 110,3 36 63,6 48,6 15,8 716 1960 18,4 54,3 52,2 70,4 39,7 121,7 113,3 30,6 5,4 22,6 13,9 0,6 543 1961 14,5 19,9 12,2 20,2 50,8 32,2 77,6 9,8 6,9 36,2 44 11,8 336 1962 1,8 21,9 25 102,6 59,2 44,2 46,9 41,6 16,7 18 26,3 38,8 443 1963 24,5 23,5 47,5 119,5 146,9 176,2 122,9 70,1 56,9 70,6 25,2 18 902 1964 12,8 34,3 42,4 30,8 72,6 116,7 39,8 41 3 27,5 12,3 8,6 442 1965 9,2 18,6 50,7 85,7 83,4 114,4 71,5 70,3 20,2 99,9 18,8 0,4 643 1966 15,7 25,8 17,7 66,9 153,5 94 23,7 10,4 95,9 10,8 14,1 23,6 552 1967 24,5 28,7 21,3 49 83,5 60,3 94 29,6 90,7 14,7 63,4 48,3 608 1968 45,8 58,3 41,4 86,1 71,9 117,5 30,3 49,4 24 48,3 8,8 29,3 611 1969 59,4 33,6 56 96,8 128,9 60,6 32,8 12,4 65,1 73,2 24,7 22,7 666 1970 17,4 25 41,2 14,4 62,9 44,1 30,2 90,3 8,8 39,2 23,7 41,6 439 1971 14,3 19,3 37,9 43 74,5 97,6 6,2 38,1 19 63,1 23,3 17,6 454 1972 33,8 33,8 52,4 37,5 167,3 130,7 34 10,3 73,4 27,9 40,9 20 662 1973 34,3 17 62,2 47,6 80,3 149 68,5 7,9 13,9 51,7 92,1 20,2 645 1974 22,8 16 60,9 91,4 105,5 109,2 59,9 67,4 92,1 0 25,5 4,2 655 1975 11,5 74,3 29,3 32,4 147,4 46,9 26 45,9 49,9 56,6 46,2 15,7 582 1976 25,2 66,9 23,6 75,1 119,4 90 72,9 5,3 44,1 60,6 10 21,2 614 1977 38 13,1 68,5 43,8 72,2 105,1 50,2 34,7 56,3 38 3,4 20,2 544 1978 40,7 49,6 67,6 101,2 116,1 103,5 7,2 21,1 13,2 25,1 18,3 26,4 590 1979 12,4 48 31,4 79,9 80,5 125,9 40,5 25,1 11,9 93 53,2 16,3 618 1980 41,8 20,5 51,2 84,2 54 46,9 47,5 65,7 26,6 41,2 21,6 3,4 505 1981 5,3 33,9 43,2 67,5 90,4 78,8 24,9 92,8 6,2 38,5 59,5 17,3 558 1982 18,5 31,4 79,2 42,2 97,2 99,5 56,2 30,3 82,1 46,3 47,2 0 630
56 Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec TOTAL 1983 10,6 14,7 20,6 36,1 121,6 223,1 76,1 91,6 25,1 35,5 46,1 7,6 709 1984 20,7 49 74 70,3 54,6 35,9 60,6 28,5 10,3 54,7 17,9 24,9 501 1985 23,9 36,6 22,1 41,1 100,2 81,8 62,8 5,2 9,5 42,1 16,2 13,7 455 1986 21,3 46,4 42,6 41,4 171,4 95,5 50,7 15,4 40 52,8 87,2 11,5 676 1987 46,3 41,1 44,1 69,2 42,4 26 9,5 111 30,1 104,8 44,9 63,5 633 1988 17,5 36,1 55,8 59,1 163,2 108,9 84,6 54,8 35,4 71,6 29,2 5,4 722 1989 0,6 13,8 20,2 29,6 42,5 47,9 57,6 57,4 49,7 117,6 49,8 25,1 512 1990 28,5 46,6 17,1 97,9 67,8 80,3 31,2 39,4 19,9 63,9 44,6 38,6 576 1991 37,9 14,1 90,1 70,8 108,5 77,5 59,7 26,5 3,9 36,4 60,3 25,5 611 1992 13,1 44 8,1 37,8 83,9 119,8 56,5 20,7 94,1 20,7 52 38 589 1993 19 33,8 9,4 49,1 82,3 83,7 15,5 49,5 39,2 22,2 74,6 10 488 1994 6 44,2 74,1 67,2 68,6 74,3 46,5 37,5 11,1 26,2 81,6 36 573 1995 12,7 14 44,5 64,4 35,6 103,7 59,3 22,6 86,1 37,8 23,7 10,4 515 1996 15,3 42,4 40,1 86,7 77,8 64,1 30,7 9,9 45,7 47,2 1,6 26,4 488 1997 1998 10,0 12,0 18,0 50,0 129,0 93,0 80,0 9,0 44,0 31,0 44,0 19,0 539 1999 0,0 19,0 48,0 37,0 85,0 102,0 58,0 25,0 90,0 31,0 37,0 6,0 538 2000 67,0 13,0 53,0 10,0 106,0 27,0 1,0 3,0 19,0 21,0 14,0 21,1 355 2001 14,4 15,2 59,2 79,7 46,0 43,0 31,0 30,0 8,0 10,0 7,0 14,0 358 2002 15,6 18,9 37,4 96,2 105,7 140,7 105,4 179,1 21,3 49,5 5,7 36,5 812 2003 7,9 39,1 93,3 86,2 57,4 90,2 54,6 16,8 19,6 101,8 53,9 26,2 647 2004 9,4 42,7 60,7 122,2 137,3 150,8 75,7 56,3 35,7 49,5 60,9 0,0 801 2005 8,8 30,3 79,1 59,1 108,3 130,1 25,3 47,2 73,7 78,6 23,7 16,9 681 2006 15,6 18,9 37,4 96,2 105,7 140,7 105,4 179,1 21,3 49,5 5,7 36,5 812
57 Annex 10. Average Monthly Temperature, Berd
Average Monthly Air Temperature Recorded in Berd Meteorological Post, in 0C
Year Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec AVE 1934 ------14,9 10,7 5,1 -0,2 1935 -3,5 1,7 1,6 10,6 14,0 16,6 20,5 21,6 18,1 13,1 3,2 3,0 10,04 1936 -0,6 0,0 2,3 10,1 12,1 17,1 20,8 20,2 15,1 11,0 6,2 0,4 9,56 1937 -3,4 0,9 3,2 8,0 12,5 14,8 21,8 21,7 18,9 12,1 6,9 4,4 10,15 1938 0,6 0,1 1,0 9,9 13,5 16,7 21,2 20,6 16,5 11,3 5,6 0,7 9,81 1939 0,9 0,3 2,0 8,1 16,2 18,7 20,0 18,9 15,0 10,6 4,5 2,8 9,83 1940 -1,7 0,4 2,2 11,1 11,8 17,6 20,9 22,4 16,6 10,6 7,1 4,7 10,31 1941 -0,4 4,6 3,6 9,9 15,3 19,3 21,9 19,3 17,5 9,6 3,2 -0,3 10,29 1942 -1,2 -1,3 1,3 7,4 14,3 19,1 21,1 20,9 15,5 11,1 5,5 0,8 9,54 1943 -1,8 -2,7 -1,3 9,1 14,2 16,9 19,6 20,2 15,3 11,5 6,1 2,4 9,13 1944 -0,5 3,2 7,7 8,5 13,6 17,8 20,3 18,2 16,2 10,9 6,3 -2,4 9,98 1945 -1,0 -2,8 1,8 7,5 13,4 15,9 20,5 20,5 16,3 10,3 4,1 1,6 9,01 1946 -1,3 2,5 3,2 8,3 14,3 16,6 18,9 19,0 17,3 8,4 5,8 -1,3 9,31 1947 -1,3 0,9 7,7 9,2 12,6 17,7 20,8 19,2 14,5 9,5 7,8 3,7 10,19 1948 3,6 2,6 0,2 5,7 14,7 19,2 21,6 21,4 14,3 9,4 3,7 -2,7 9,48 1949 -2,9 -3,0 1,2 6,8 13,4 19,2 22,5 19,3 13,9 7,8 5,9 0,7 8,73 1950 -5,6 -1,1 3,5 11,6 15,0 16,1 19,5 19,3 17,5 10,2 4,3 2,3 9,38 1951 -0,6 -1,8 6,1 11,8 14,1 17,5 21,4 21,3 15,3 6,7 5,6 0,7 9,84 1952 0,5 1,2 3,1 7,0 13,5 15,6 21,0 21,4 16,8 13,9 4,3 2,6 10,08 1953 2,0 2,6 1,1 8,4 14,5 18,3 19,9 20,7 15,3 12,0 0,6 -3,0 9,37 1954 -2,4 -3,5 0,9 6,5 14,7 18,1 21,7 21,5 16,6 12,5 6,3 3,5 9,70 1955 2,2 4,6 2,9 8,4 14,0 18,3 20,9 19,8 15,9 12,5 6,3 1,6 10,62 1956 1,2 -1,6 -0,1 9,4 11,3 17,4 19,5 20,7 13,5 9,5 4,0 -1,3 8,63 1957 -3,1 2,1 3,1 9,8 15,2 17,7 20,1 21,1 18,8 10,8 5,1 2,8 10,29 1958 2,0 3,0 4,4 9,3 17,4 20,0 21,2 21,8 16,2 10,6 3,5 2,6 11,00 1959 3,2 -2,3 1,9 11,6 14,9 17,5 22,3 20,8 14,9 8,7 4,4 1,6 9,96 1960 3,1 2,3 1,4 8,5 15,1 19,1 21,2 20,6 17,7 13,4 7,7 3,5 11,13 1961 0,2 1,3 4,8 11,8 18,8 21,6 22,3 22,4 15,9 10,4 7,6 4,9 11,83 1962 1,6 2,7 7,8 9,2 15,9 19,9 24,5 22,1 17,8 11,9 6,6 4,8 12,07 1963 3,3 3,3 3,6 9,7 13,8 17,7 21,3 20,0 17,5 12,5 6,4 2,4 10,96 1964 -3,1 0,4 4,6 9,0 15,5 20,3 21,0 20,0 17,3 11,3 7,0 1,3 10,38 1965 -1,0 1,1 5,0 7,6 16,0 19,4 21,5 21,9 17,3 9,1 7,7 4,9 10,88 1966 5,9 5,6 6,7 12,0 14,2 19,2 24,3 23,8 16,8 13,1 9,3 4,6 12,96 1967 2,3 -1,2 3,9 8,7 15,7 17,7 20,6 22,0 16,1 13,0 6,9 3,2 10,74 1968 1,3 1,7 4,0 9,9 17,2 17,4 22,2 20,8 19,2 12,4 8,2 1,9 11,35 1969 -3,3 -3,5 2,4 8,3 15,7 20,9 20,6 21,7 16,9 10,9 7,2 4,9 10,23 1970 2,0 4,2 6,2 13,1 16,2 19,2 22,1 18,1 14,4 8,7 7,5 0,4 11,01 1971 1,1 -0,4 4,9 9,2 17,0 18,6 24,8 21,6 20,4 11,3 8,0 1,7 11,52 1972 -5,1 -3,2 2,7 13,6 14,8 19,7 23,3 22,4 17,8 14,7 7,3 -0,9 10,59 1973 -2,4 4,6 4,9 10,4 15,5 18,0 21,6 21,9 16,0 13,2 5,2 2,3 10,93 1974 -2,1 -0,1 4,2 7,6 15,8 19,8 21,0 19,8 15,6 14,7 7,7 3,0 10,58 1975 0,9 -0,4 4,7 13,2 16,1 21,7 24,3 22,5 17,6 10,7 5,3 1,4 11,50 1976 1,8 -3,8 1,9 11,4 14,3 18,1 21,4 23,2 16,9 10,3 5,9 3,3 10,39 1977 -4,3 4,3 6,1 12,2 16,0 20,0 22,1 22,1 17,3 8,8 7,7 0,8 11,09 1978 0,9 1,4 6,7 9,3 14,2 17,0 23,1 21,0 19,6 12,8 4,4 4,1 11,21 1979 0,9 3,3 7,1 11,0 15,8 18,5 22,2 23,8 20,0 12,5 7,6 3,3 12,17 1980 -1,3 0,1 4,1 10,7 15,9 20,7 24,3 21,2 17,0 10,7 8,6 5,2 11,43 1981 3,3 3,3 5,9 8,9 13,1 19,1 22,6 20,8 18,5 13,5 6,5 5,7 11,77
58 Year Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec AVE 1982 0,5 -0,8 3,0 12,7 15,2 18,3 21,9 20,2 17,5 10,8 4,2 1,9 10,45 1983 -1,0 3,0 3,9 12,2 15,9 18,2 23,2 20,5 16,1 11,3 6,8 2,4 11,04 1984 1,7 -3,9 4,3 10,2 14,1 19,0 23,6 20,8 18,9 11,8 6,2 -1,4 10,44 1985 1,3 0,2 2,3 11,9 17,0 19,9 20,4 22,4 18,6 10,4 8,5 1,7 11,22 1986 2,4 0,9 2,5 12,2 13,1 19,2 22,9 23,3 20,2 11,5 5,1 1,8 11,26 1987 2,7 2,8 2,8 8,2 16,8 21,1 22,9 21,6 16,6 8,3 5,5 2,4 10,98 1988 0,2 0,3 4,7 11,2 13,2 19,6 22,2 20,2 16,9 11,8 5,4 3,6 10,78 1989 -0,5 0,6 7,9 14,2 15,9 20,6 23,8 23,4 18,0 11,6 6,4 2,2 12,01 1990 -0,5 2,2 6,3 9,9 13,2 19,9 23,1 21,1 19,1 12,3 9,0 2,5 11,51 1991 0,1 0,1 4,6 11,6 - 20,0 23,9 22,3 17,8 14,3 6,4 1,1 1992 -1,5 -1,0 - - - - 22,0 22,2 18,9 14,4 7,8 - 1993 - 0,4 7,2 - 16,4 22,1 23,3 - - - - - 1994 ------20,8 19,5 13,2 - - 1995 - - - - 16,8 20,6 22,2 23,6 18,9 11,0 8,8 1,1 1996 -1,0 1,8 - 8,6 17,2 19,3 22,2 22,4 18,2 14,9 6,5 3,9 1997 2,1 -1,1 3,7 - 18,0 22,0 22,4 22,9 15,7 14,2 6,9 3,5 1998 1,2 1,1 5,9 13,4 14,4 21,3 23,8 22,5 17,7 14,1 8,3 4,7 12,37 1999 3,5 3,9 5,4 9,6 13,8 19,0 21,7 23,0 17,9 13,7 4,6 5,2 11,78 2000 0,2 3,2 4,8 14,1 15,3 19,0 23,7 23,5 18,8 14,0 6,8 4,0 12,28 2001 0,9 2,8 7,1 13,4 14,3 19,5 20,8 22,5 17,4 11,4 7,5 4,0 11,80 2002 0,8 7,8
59 Annex 11. Average Monthly Precipitation, Berd
Average Monthly Atmospheric Precipitation Recorded in Berd Meteorological Post, in mm
Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec TOT 1935 34,3 18,1 45 31,6 63,3 133,9 40,7 27,3 15 41,2 50,6 13,9 515 1936 5,3 18,7 33,2 52,6 179,5 105,9 104,7 8,8 67,7 61,1 32,8 7,9 678 1937 21,4 8 46,8 84,1 118,4 96,9 27,5 22,6 18 16,8 30,5 19,8 511 1938 13,4 53,5 18,8 57,7 153,8 35,6 43,3 23,3 79,9 14,3 6,5 23,1 523 1939 6,3 14,7 33,2 40,9 25,3 99,9 78 88 25,2 54,6 41,4 10,9 518 1940 34,8 24 33,7 43,1 72,8 111,6 66,6 7,3 45,2 67 13,8 24,2 544 1941 32 20,9 59,2 76,5 73,2 29,6 43,7 26,4 39,5 10,1 60,9 11,9 484 1942 30,2 22,2 65 54,5 130,1 36,7 40,5 3,2 29,2 57,3 44,4 19,1 532 1943 12,8 32,4 54,9 2,5 70,7 96,6 80,5 34,9 33,7 31,2 24,7 18,4 493 1944 20 6,7 13,6 58,8 84 88,7 73,5 56,4 37,7 43,3 18,4 16,1 517 1945 6,7 27,1 30,5 55,2 88,2 92,4 32,3 18,6 19 31,9 25,1 13,7 441 1946 24,1 29 39 46 123,2 131,1 61,4 49 29,8 117,7 21,7 31,9 704 1947 33 14,2 31,1 96,3 116 81,2 25,1 65,1 108,9 23,9 42,1 11 648 1948 14,7 30,7 83,2 63 82,1 55,9 8,5 9,1 40,9 62,3 45 25,5 521 1949 4,6 17,5 47,8 25,5 137,1 42,4 4,5 70 70 35,2 14,1 11,7 480 1950 46,1 18,1 50,3 13,5 91,7 125,4 24 31,5 1,6 108,5 38,2 6,4 555 1951 35,1 21,8 5,6 46 97 96,6 47,5 6,8 94,4 151,8 18,8 31,3 653 1952 2,6 25,5 43 68,8 91,5 193,5 0 7,2 6,6 6,3 14,6 14,4 474 1953 10,5 22,7 38,9 52,8 62,3 132,9 109,3 57,5 8,1 - 98,4 19,3 1954 13,1 29 52,8 76,5 73,2 116,1 43 23,1 50,1 16,3 28,5 18,9 541 1955 8,2 11,6 86,3 54,6 161,2 80,9 48,7 58,3 71,4 10,2 59 21 671 1956 10,7 38,6 75,4 71,9 162,7 31 58,4 7,8 58,9 36 35,5 29,7 617 1957 32,7 10,7 43,9 10,5 70,3 30,5 112,5 52,2 17,4 4,4 14,4 5 405 1958 14,7 8,4 34,1 56,1 15,8 76,6 78,7 12,9 53,8 38,2 22,6 17,2 429 1959 8,7 17,7 78,2 12 90,1 114,7 15,8 138,9 32,9 44 46,1 23,4 623 1960 14,3 40,9 68,3 100 21,9 53,4 99,3 17,9 5,2 18,8 11,2 0,2 451 1961 13,7 18,9 8,4 11,7 28,6 27,5 63,1 3,7 12,6 44,9 39 8,4 281 1962 6,9 11,2 19,4 62 32,5 28,1 46,5 33,5 30,7 38,1 23,9 39,2 372 1963 23 15,8 35,9 60,9 118,6 145,5 96 52,6 39,1 46 34,6 20,7 689 1964 10 30,3 37,7 35,4 58,6 94 28,8 37,3 5 12,8 3,2 9,7 363 1965 13,3 13 33,9 53 44,3 58,4 66,2 11,5 29,3 52,7 9,9 2,8 388 1966 20,9 13,6 16,8 43,3 117,8 56,7 12,2 9 65 16,4 13,6 17,6 403 1967 11,3 33,1 18,4 35,8 47,9 39,8 46,6 14,5 70,6 9,2 64,1 36,9 428 1968 19,2 43,5 39,7 77,7 32,7 72,9 19,5 17,1 20 27,6 21,6 26,7 418 1969 64,4 36 66 92,4 101,4 42,4 16,4 17,5 55,6 57 12,9 22,8 585 1970 10,6 19,4 55,3 13,8 60,8 35,5 32,1 117,1 10,7 29,9 21,7 40,9 448 1971 17 13,7 29,8 29,8 52,1 55,8 6,8 27,3 18,2 40,1 9,7 16,7 317 1972 34,8 34,9 37,1 36,7 135,5 84,7 45,5 6 89,9 28,8 21,1 17,5 573 1973 34,7 21,7 52,3 45,5 75,6 114,2 52,7 7,5 15,3 38,4 64,7 10 533 1974 24,6 21,4 68,9 82,4 79,4 85,6 36,6 78,2 140,8 0,4 32,4 6,1 657 1975 12 63,2 17,7 35,7 90,4 41,4 33,9 17,2 52,9 54,9 42 13,6 475 1976 21,2 63,1 20,7 59 130,4 98,2 47,3 6,2 48,7 53 11,6 17,4 577 1977 40 11,6 52,4 35,2 43,5 45,1 35,3 28,6 52,7 27,6 7,1 11,2 390 1978 41,4 32,5 49,5 96,8 111 76,9 0,5 23,2 5,5 19,1 18,5 25 500 1979 11,1 25,2 38,3 50 63,6 95,5 49 6,5 5,3 80,5 31,3 9,9 466 1980 39,8 15,4 44,6 64,1 40,1 54,2 91,7 32,5 11 39,9 18,9 2,1 454 1981 3,4 25,9 36,7 50,2 95,7 137,4 45,3 75,6 5,7 28 49,2 15,4 569 1982 11,1 44,5 83,7 53,4 71 77 64 43,3 51 39,6 44,7 7 590
60 Year Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec TOT 1983 16,2 11,9 18,4 46,8 60,4 133 45,5 93,2 30,5 41,7 14,7 7,3 520 1984 24,9 48 49,9 48,1 54,5 30 27,7 29,3 16 49,3 35,6 30,7 444 1985 18,5 27,6 17,4 27 58,2 67,5 35,7 13 7,5 47,9 16,8 9,6 347 1986 18,9 24,7 32,7 22,6 92 45,6 17 14,4 32,8 49,1 44,3 3,9 398 1987 30 24,7 51,3 64,9 37,4 8,5 20,4 53,5 27,7 103 26,3 42,7 490 1988 19 19,3 32,6 46 135,4 75,8 51,1 35,3 14,2 66,7 28 2,6 526 1989 3 9,1 10,1 15,4 26,1 39,7 36,5 13 47,9 90,2 35,7 22,4 349 1990 23,4 24,8 11,3 83,4 79,4 27,3 18,1 18,1 7,6 37,3 23,1 19,9 374 1991 40,4 6,3 82,3 39,7 93,7 16,1 28 11,3 1,0 20,7 13,3 25,5 378 1992 18,7 33,9 - - - - 13,2 1,7 22,3 29,1 29 34,5 1993 - 26,7 3,4 - 40,7 31,7 9,3 - - - - - 1994 ------6,5 2,5 12,6 - - 1995 32,5 51,4 49,1 10,9 18,6 15,1 13,1 7,2 198 1996 14,8 32,4 28,3 30,3 33,2 15,1 9,2 12,3 44,8 23,6 0 26,6 271 1997 1998 11 4 11 19 75 36 15 8 0 1 32 5 217 1999 4 5 23 26 57 100 30 29 32 17 15 0 338 2000 30 16 20 4 80 39 0 33 10 37 5 15 289 2001 32 1 27 84 98 28 5 20 0 10 1 17 323 2002 9 6,8
61 Annex 12. Comparison of the Average Monthly Temperatures, Ijevan
Comparative analysis of the average monthly temperatures (0C), Ijevan meteorological post
Month Average monthly temperature Average monthly temperature for Difference for the period 1961-1990 the period 1991-2006 January 1,01 1,79 +0,78 February 1,62 2,56 +0,94 March 4,89 5,90 +1,01 April 10,60 10,98 +0,38 May 14,88 14,90 +0,02 June 18,54 19,35 +0,81 July 21,82 22,42 +0,6 August 21,10 22,81 +1,71 September 17,37 17,88 +0,51 October 11,60 12,79 +1,19 November 7,17 6,86 -0,31 December 3,46 3,30 -0,16
Comparison of average air temperature (in 0C) for the periods 1961-1990 and 1991-2006 by individual months recorded in Ijevan meteorological station
Average January T for individual years (1991-2006) Average February T for individual years (1991-2006) Average January T for 1960-1990 Average February T for 1960-1990
5,00 8,00 4,00 6,00 3,00 2,00 4,00
1,00 2,00
Temperature 0,00 Temperature 0,00 -1,00 -2,00 -2,00 1991-2006 1991-2006
Average March T for individual years (1991-2006) Average April T for individual years (1991-2006) Average March T for 1960-1990 Average April T for 1960-1990
10,00 16,00 14,00 8,00 12,00 6,00 10,00 8,00 4,00 6,00 Temperature Temperature 4,00 2,00 2,00 0,00 0,00 1991-2006 1991-2006
Average May T for individual years (1991-2006) Average June T for individual years (1991-2006) Average May T for 1960-1990 Average June T for 1960-1990
20,00 25,00
20,00 15,00 15,00 10,00 10,00 Temperature Temperature 5,00 5,00
0,00 0,00 1991-2006 1991-2006
62
Average July T for individual years (1991-2006) Average August T for individual years (1991-2006) Average July T for 1960-1990 Average August T for 1960-1990
30,00 30,00
25,00 25,00
20,00 20,00
15,00 15,00
10,00 10,00 Temperature Temperature 5,00 5,00
0,00 0,00 1991-2006 1991-2006
Average September T for individual years (1991-2006) Average October T for individual years (1991-2006) Average September T for 1960-1990 Average October T for 1960-1990
25,00 16,00 14,00 20,00 12,00 15,00 10,00 8,00 10,00 6,00 Temperature Temperature 4,00 5,00 2,00 0,00 0,00 1991-2006 1991-2006
Average November T for individual years (1991-2006) Average December T for individual years (1991-2006) Average November T for 1960-1990 Average December T for 1960-1990
10,00 7,00 6,00 8,00 5,00 4,00 6,00 3,00 2,00 4,00 1,00 Temperature Temperature 2,00 0,00 -1,00 0,00 -2,00 1991-2006 1991-2006
63 Annex 13. Comparison of Average Precipitation, Ijevan
Comparative analysis of the average monthly precipitation (mm), Ijevan meteorological post
Month Average monthly precipitation Average monthly precipitation Difference for the period 1961-1990 for the period 1991-2006 January 22,97 16,85 -6,12 February 33,94 26,77 -7,17 March 42,31 50,16 7,85 April 62,35 67,51 5,16 May 94,25 89,14 -5,11 June 92,40 96,04 3,64 July 51,94 53,64 1,70 August 42,05 47,48 5,43 September 35,56 40,85 5,29 October 49,87 40,83 -9,04 November 33,95 36,38 2,43 December 19,89 21,50 1,61
Comparison of average precipitation (in mm) for the periods 1961-1990 and 1991-2006 by individual months recorded in Ijevan meteorological station
Average January P for individual years (1991-2006) Average February P for individual years (1991-2006) Average January P for 1960-1990 Average February P for 1960-1990
80,00 50,00 70,00 60,00 40,00 50,00 30,00 40,00 30,00 20,00 20,00
Precipitation, mm Precipitation, 10,00 10,00 mmPrecipitation, 0,00 0,00 1991-2006 1991-2006
Average March P for individual years (1991-2006) Average April P for individual years (1991-2006) Average March P for 1960-1990 Average April P for 1960-1990
100,00 140,00 120,00 80,00 100,00 60,00 80,00
40,00 60,00 40,00
20,00 Precipitation, mm Precipitation, mm Precipitation, 20,00 0,00 0,00 1991-2006 1991-2006
64 Average May P for individual months (1991-2006) Average June P for individual years (1991-2006) Average May P for 1960-1990 Average June P for 1960-1990
160,00 160,00 140,00 140,00 120,00 120,00 100,00 100,00 80,00 80,00 60,00 60,00 40,00 40,00 Precipitation, mm Precipitation, Precipitation, mm Precipitation, 20,00 20,00 0,00 0,00 1991-2006 1991-2006
Average July P for individual years (1991-2006) Average August P for individual years (1991-2006) Average July P for 1960-1990 Average August P for 1960-1990
120,00 200,00
100,00 150,00 80,00
60,00 100,00 40,00 50,00 Precipitation, mm Precipitation, mm Precipitation, 20,00 0,00 0,00 1991-2006 1991-2006
Average September P for individual years (1991-2006) Average October P for individual years (1991-2006) Average September P for 1960-1990 Average October P for 1960-1990
100,00 120,00
80,00 100,00 80,00 60,00 60,00 40,00 40,00 20,00 Precipitation, mm Precipitation, Precipitation, mm Precipitation, 20,00
0,00 0,00 1991-2006 1991-2006
Average November P for individual years (1991-2006) Average December P for individual years (1991-2006) Average November P for 1960-1990 Average December P for 1960-1990
100,00 40,00 35,00 80,00 30,00 25,00 60,00 20,00 40,00 15,00 10,00
20,00 Precipiation, mm Precipitation, mmPrecipitation, 5,00 0,00 0,00 1991-2006 1991-2006
65 Annex 14. Comparison of Average Air Temperature, Berd
Comparative analysis of the average monthly temperatures (0C), Berd meteorological post
Month Average monthly temperature Average monthly temperature for Difference for the period 1961-1990 the period 1991-2002 January 0,41 0,69 0,28 February 1,05 1,90 0,85 March 4,56 5,53 0,97 April 10,59 11,78 1,19 May 15,39 15,78 0,39 June 19,33 20,31 0,98 July 22,46 22,60 0,14 August 21,52 22,57 1,05 September 17,59 18,08 0,49 October 11,60 13,52 1,92 November 6,87 7,07 0,20 December 2,70 3,44 0,74
Comparison of average air temperature (in 0C) for the periods 1961-1990 and 1991-2002 by individual months recorded in Berd meteorological station
Average January T for individual years (1991-2002) Average February T for individual years (1991-2002) Average January T for 1960-1990 Average February T for 1960-1990
4,00 10,00
3,00 8,00
2,00 6,00
1,00 4,00
0,00 2,00 Temperature Temperature -1,00 0,00
-2,00 -2,00 1991-2002 1991-2002
Average March T for individual years Average March T for 1960-1990 Average April T for individual years (1991-2002) Average April T for 1960-1990 8 7 16,00 6 14,00 12,00 5 10,00 4 8,00 3 6,00 Temperature
2 Temperature 4,00 1 2,00 0 0,00 1991-2002 1991-2002
Average May T for individual years (1991-2002) Average June T for individual years (1991-2002) Average May T for 1960-1990 Average June T for 1960-1990
20,00 23,00
22,00 15,00 21,00
10,00 20,00 19,00 Temperature Temperature 5,00 18,00
0,00 17,00 1991-2002 1991-2002
Average July T for individual years (1991-2002) Average August T for individual years (1991-2002) Average July T for 1960-1990 Average August T for 1960-1990
25,00 24,00
24,00 23,00 23,00 22,00 22,00 21,00 21,00 Temperature Temperature 20,00 20,00
19,00 19,00 1991-2002 1991-2002
Average September P for individual years (1991-2002) Average October T for individual years (1991-2002) Average September T for 1960-1990 Average October T for 1960-1990
25,00 16,00 14,00 20,00 12,00 15,00 10,00 8,00 10,00 6,00 Temperature Temperature 4,00 5,00 2,00 0,00 0,00 1991-2002 1991-2002
Average November T for individual years (1991-2002) Average December T for individual years (1991-2002) Average November T for 1960-1990 Average December T for 1960-1990
10,00 6,00
8,00 5,00 4,00 6,00 3,00 4,00 2,00 Temperature Temperature 2,00 1,00
0,00 0,00 1991-2002 1991-2002
67 Annex 15. Comparison of Average Precipitation, Berd
Comparative analysis of the average monthly precipitation (mm), Berd meteorological post
Month Average monthly precipitation Average monthly precipitation Difference for the period 1961-1990 for the period 1991-2006 January 21,05 19,95 -1,10 February 27,06 14,69 -12,37 March 37,26 27,84 -9,42 April 50,94 33,75 -17,19 May 70,86 63,76 -7,10 June 66,05 39,66 -26,39 July 41,53 17,64 -23,89 August 30,76 14,74 -16,02 September 33,06 14,58 -18,48 October 41,58 18,46 -23,12 November 26,82 13,55 -13,27 December 16,67 16,35 -0,32
Comparison of average atmospheric precipitation (in mm) for the periods 1961-1990 and 1991- 2002 by individual months recorded in Berd meteorological station
Average January P for individual years (1991-2002) Average February P for individual years (1991-2002) Average January P for 1960-1990 Average February P for 1960-1990
50,00 40,00 35,00 40,00 30,00 30,00 25,00 20,00 20,00 15,00
10,00 10,00 Precipitation, mm Precipitation, Precipitation, mm 5,00 0,00 0,00 1991-2002 1991-2002
Average March P for individual years (1991-2002) Average April P for individual years (1991-2002) Average March P for 1960-1990 Average April P for 1960-1990
100,00 100,00
80,00 80,00
60,00 60,00
40,00 40,00
20,00 Precipitation, mm 20,00 Precipitation, mm Precipitation, 0,00 0,00 1991-2002 1991-2002
Average May P for individual years (1991-2002) Average June P for individual years (1991-2002) Average May P for 1960-1990 Average June P for 1960-1990
120,00 120,00
100,00 100,00
80,00 80,00
60,00 60,00
40,00 40,00
Precipitation, mm 20,00 Precipitation, mm 20,00 0,00 0,00 1991-2002 1991-2002
Average July P for individual years (1991-2002) Average August P for individual years (1991-2002) Average July P for 1960-1990 Average August P for 1960-1990
60,00 35,00
50,00 30,00 25,00 40,00 20,00 30,00 15,00 20,00 10,00 Precipitation, mm
Precipitation, mm 10,00 5,00
0,00 0,00 1991-2002 1991-2002
Average September P for individual years (1991-2002) Average October P for individual years (1991-2002) Average September P for 1960-1990 Average October P for 1960-1990
50,00 50,00
40,00 40,00
30,00 30,00
20,00 20,00
10,00
10,00 Precipitation, mm Precipitation, mm
- 0,00 1991-2002 1991-2002
Average November P for individual months (1991-2002) Average December P for individual years (1991-2002) Average November P for 1960-1990 Average December P for 1960-1990
35,00 40,00 30,00 35,00 30,00 25,00 25,00 20,00 20,00 15,00 15,00 10,00 10,00 Precipitation, mm Precipitation, Precipitation, mm 5,00 5,00 0,00 0,00 1991-2002 1991-2002
69 Annex 16. Data on Extremes in Average Monthly Temperature and Precipitation, Ijevan
Data on years when the average monthly air temperature diverts more than by 50% from the corresponding month of 1961-1990
Months Years when the average monthly air Years when the average monthly air temperature exceeded temperature proceeded January 1994, 1995, 1997, 1999, 2001, 2003, 1992, 1993, 1996, 2000, 2006 2004, 2005 February 1995, 1996, 1999, 2000, 2001, 2002, 1991, 1992 2004, 2006 March 2001, 2002, 2004 December 1996, 1999 1992, 1994, 2002, 2006
Data on years when the average monthly precipitation diverts more than by 50% from the corresponding month of 1961-1990
Months Years when the average monthly Years when the average monthly precipitation exceeded proceeded January 1991, 2000 1994, 1998, 1999, 2003, 2004, 2005 February 1991, 1995, 1998, 2000, 2001 March 1991, 1994, 2003, 2005 1992, 1993, 1998 April 2002, 2004, 2006 2000 May 1995, 2001 June 2002, 2006 2000, 2001 July 1998, 2002, 2004, 2006 1993, 2000, 2005 August 2002, 2006 1992, 1995, 1996, 1998, 2000, 2003 September 1992, 1995, 1999, 2005 1991, 1994, 2001 October 2003, 2005 1992, 1993, 2000, 2001 November 1991, 1993, 1994, 2003, 2004, 1996, 2000, 2001, 2002, 2006 December 1992, 1994, 2002, 2006 1993, 1999, 2004
70 Annex 17. Bibliography
[1] “Republic of Armenia - Tavush Marz: Lusadzor Community: Integrated Development Plan 2007-2008“, prepared within UNDP Community Development Project, Yerevan, 2007.
[2] Draft Report “Assessment of Vulnerability of Armenia’s Water Resources due to Climate Change” prepared within UNDP “Enabling Activities for Preparation of Armenia’s Second National Communication to UNFCC” Project, Yerevan, 2008.
[3] Final Report “Model Simulations of Climate Change over Armenia Region” prepared within UNDP “Enabling Activities for Preparation of Armenia’s Second National Communication to UNFCC” Project, Yerevan, 2008.
[4] World Bank/ArmStateHydromet, “Republic of Armenia: Assessment of Economic Efficiency of Hydrometeorological Service”, in Russian, Yerevan, 2007.
[5] ArmStateHydromet, “Assessment of Systematic Observations of Climate Change in the Republic of Armenia”, Yerevan, 2007.
71