Journal of Spatial Analysis

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Climate is one of the important natural factors that affect all stages of life, particularly human exploitation. Selection of the type of clothing, housing, cultures, architecture, civil engineering, and settlements are influenced by climatic factors. It can be said that the climatic circumstances of the surface of the earth and atmospheric circulation patterns have an important role in shaping and organizing the environment (Alijani, 2009). In some cases, the normal weather conditions become abnormal and cause many damages, which are mostly catastrophes rooted in climatic changes, such as hail, frost, heat and cold waves, floods, storms and so on. Blizzard is one of the atmospheric phenomena, which happens as the result of snow combined with wind (15 meters per second), and low temperatures (below zero°C), and it causes severe losses.

Due to its special geographical location, Iran is placed in the transition region of the large-scale patterns of common tropospheric circulation, and is the intersectional place of the of extra-tropical and tropical circulation system. This feature along with its complex topography caused the land to have a considerable climatic diversity. The climatic diversity makes the various climatic phenomena to be observed with intensity, energy, and different frequencies, therefore, the climatic phenomena with high intensity always causes damage to natural resources and the human civilization. This undesirable phenomenon is called climatic risks. Since the West Azerbaijan Province is located in mountainous areas and high latitudes, the feature is triggered many climatic risks such as flood, hail, snow, snow storm, and so on. Therefore, snowstorm is one of such phenomena that have occurred every year or every few years due to the specific characteristics of the region and have caused damages in the fields of transportation, energy, livestock, closeness of schools and offices.

The purpose of this study is the statistical and synoptic analysis of snowstorm in west Azerbaijan province. Therefore, the data related to the present weather codes were collected during the period 1986 to 2009 from the National Meteorological Agency. The data related to the weather codes entered in Excel, and data related to the snowstorm were selected through Filter tool and isolation of codes related to the strong snowstorms (codes 37and39) and weak snowstorms (codes 36 and 38). Then the data related to the snowstorm was entered in SPSS, and the statistical analysis was performed. In the next step, three cases of the strong and common snowstorm (code 37 and 39) were selected for synoptic analysis. Then, the synoptic maps of the different layers of the atmosphere were selected as the samples for strong snowstorm for the days before the event of the phenomenon, the day of event, and the day after the event of the phenomenon by the using of the accuracy of 2.5 degrees from cdc.noaa.gov website. The study area has been selected in 10 to 80 degrees north latitude, and 15 to 90 degrees east longitude for identifying the patterns that affect West Azerbaijan Province. The data was received on wind speed and direction in digits from the National Center for Environmental Prediction. Then, the maps of the wind direction and speed were provided in Grads. Finally, the daily analysis and interpretation of pressure (500hPa at sea level), instability (700hPa level and the ground level), Earth's surface temperature, wind speed and direction maps for 700hPa level, and identification of patterns that have caused snowstorm in West Azerbaijan province were carried out. Statistical and synoptic analysis of snowstorm phenomenon in West Azerbaijan province during was performed in the period 1986 to 2009. To do this, using codes 36 to 39, which represent a variety of snowstorm (weak and strong), the frequency of snowstorm days on monthly and annual average, distribution of the snowstorm in the extracted stations, the frequency of strong snowstorms (codes 37and39), weak snowstorms (codes 36 and 38), all types of snowstorms (codes 36 to 39), and the frequency of storms in the station level were compared. Out of 322 snowstorms occurred during the period 1986 to 2009 in seven synoptic stations 108 have been determined as strong snowstorm and 214 as weak snowstorm. In order to analyze the synoptic snowstorm in West Azerbaijan province, in the first place, the strong snowstorms were identified, and then five of the strong and comprehensive storms were selected for the synoptic analysis. The snowstorms of choice are as follows: On 18 January 1986, on January 19, 2000, on February 7, 1992, on February 5, 1997, and on December 25, 1990.

For applying the study, pressure maps, Omega (700hp level at ground level), Earth's surface temperature, and wind speed and direction at 700hPa were analyzed, and patterns and conditions that are causing this phenomenon in the West Azerbaijan province were identified.

      In this study, to perform statistical and synoptic analysis of snowstorm in Western Azerbaijan province, the statistical data were examined during the period 1986 to 2009 from 7 stations, and the results of the statistical analysis showed that:

• Out of a total 322 snowstorm event days of 7 synoptic stations during the period 1986 to 2009, 108 and 214 days were strong and weak snowstorms, respectively.

• Review the annual and monthly snowstorm during the study period showed that the 1992, 1997, and 1989 with a total of 69, 29, and 25 days, as well as the 1999, 2006 and 2007 with 0, 1, and 1 day have the most and the fewest days of snowstorm, respectively. The statistical analysis showed that the snowstorm phenomena happened in January, February, March, April, November, and December. January had the most and April had the fewest snowstorms with 119 and 3 days, respectively. February with 39 days, and April and November, with the number 0 and 1 had the most and the fewest days of strong and constant snowstorms.

• Distribution of the snowstorms in the stations indicated that out of the studied seven synoptic stations, which had a great impact on the synoptic situation of the region, topography, and height, Sardasht-Maku station had the most, and stations of Khoy, Mahabad, and Orumiyeh by having no snowstorms had the fewest days of snowstorm.

• The results of the maps of the different levels of the atmosphere and Earth’s surface in the days before the storm, event day and the day after the snowstorm were selected for the snowstorm pattern, which indicated that the snowstorm in the winter due to low compliance pressure formed in the earth's surface with synoptic patterns of middle levels of the atmosphere have provided the conditions for the event, in a way that among the sample cases of the strong snowstorms occurred in the West Azerbaijan Province two circulation patterns were involved in the formation of natural hazards: The Caspian Sea low pressure pattern- Eastern Europe high pressure pattern and the north of the Black Sea low pressure pattern.

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Probability of earthquake hazard		Distance to fault lines		Relative area
		Active Faults	Passive Faults	Active Faults	Passive Faults
Very low risk	0-20	60-76	32-42	7.29	1.42
Low risk	20-40	45-60	24-32	13.82	3.96
Medium risk	40-60	30-45	16-24	16.66	8.13
High risk	60-80	15-30	8-16	21.51	22.04
Very high risk	80-100	0-15	0-8	40.72	64.45
sum		-		100

Probability of earthquake hazard		urban Settlement		Population (2011)		Relative population frequency (percent)
		Active Faults	Passive Faults	Active Faults	Passive Faults	Active Faults	Passive Faults
Very low risk	0-20	3	1	135846	17106	1.14	9.07
Low risk	20-40	6	4	86133	144021	9.62	5.75
Medium risk	40-60	10	8	739095	754968	50.43	49.37
High risk	60-80	14	18	380908	273137	18.24	25.44
Very high risk	80-100	18	20	155188	307938	20.57	10.37
sum		51		1497170		100

Probability of earthquake hazard		Rural Settlement		Population (2011)		Relative population frequency (percent)
		Active Faults	Passive Faults	Active Faults	Passive Faults	Active Faults	Passive Faults
Very low risk	0-20	162	42	54240	30236	5.51	3.07
Low risk	20-40	379	147	183718	92018	18.68	9.35
Medium risk	40-60	481	291	255412	176183	25.96	17.91
High risk	60-80	553	766	245392	340448	24.95	34.61
Very high risk	80-100	1350	1679	244942	344819	24.90	35.05
sum		2925		983704		100

1. Meticulous supervision on safety of building from the stage of plan-making to administration which have to be based on engineering principles for earthquake-prone cities including Baresar, Ataqur, Asalem, Haviq, and Roodbar which are next to active faults
2. Prevention of formation of suburbs and towns on southern and northern parts of Gilan because these parts are really vulnerable to earthquake
3. Prediction of temporary accommodation in central Gilan because this part is less vulnerable to earthquake
4. To equip buildings, hospitals, schools, and other buildings located in big cities including Rasht, Bandar-E Anzali, Fuman, and Lahijan with facilities required in case of earthquake
5. To hold training courses in rural and urban parts of the mentioned province to make residents prepared for earthquake and for emergency evacuation
6. To prioritize reformation of old and historical buildings in Rasht because Rasht is mostly laden with old buildings which are really vulnerable to earthquake

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Assessing the Homogeneity of Temperature and Precipitation Data in Iran with Climatic Approach
 
Extended Abstract:
Qualitative evaluation and validation of atmospheric parameters such as precipitation and temperature are the most important condition for statistical analysis in climatic and hydrological researches. In addition, the meteorological and climatological data have a crucial role in transportation, agriculture, urbanization and health services.  Therefore, it is clear that using wrong data source for atmospheric investigations is the first hazard in natural hazards analysis. This study aimed to investigate the homogenization of minimum and maximum temperatures and precipitation data for 36 weather stations over different climatic classes in Iran. The Standard Normal Homogeneity Test (SNHT), (Alexanderson and Moberg, 1997), Pettit test (Pettit, 1979), Cumulative Deviation test (Buishand, 1982) and Worsley’s Likelihood Ratio test (Worsley, 1979) were carried out to study homogenization of minimum and maximum temperatures and precipitation data (1966-2015). The results revealed that 91.5 % and 88.5 % of minimum and maximum temperatures data, respectively, were in non-homogenized category. Although, Isfahan, Saghez and Gorgan for minimum temperature and Bandar-e Anzali, Sharekord, Kashan and Saghez for maximum temperature showed a homogenized condition with 5 % level of significance. The results showed most of the weather stations (28 out of 36 stations) had homogenized precipitation data. Even though, seven stations including Birjandd, Kerman, Kermanshah, Saghez, Sanandaj and Tabriz had homogenized precipitation data. The Urmia weather station was in doubtful class. That is precipitation data of Urmia weather station were homogenized by two tests results and were non-homogenized with other two tests of homogenization. The spatial distribution of trend variations of minimum temperature average was between -2.8 to 2.8 degree Celsius over the country. Moreover, maximum and minimum variations of minimum temperature occurred in northeast and northwest of the country, respectively. There were a significantly increasing trend (p<0.01) in most of the regions. The results also indicated that the significant variations happened for maximum temperature in most of the weather stations, mainly in northern half of the country. The minimum temperature jump was mostly found in 1985, 1994 and 1998 years during the study period (1966-2015). The maximum variations of minimum temperature were in Mashhad, Shahroud, Ahvaz, Yazd and Semnan weather stations with 2.8, 2.3, 2.2, 2 and 2 degrees Celsius, respectively, jump for above mentioned years during 1966-2015. In addition, the minimum change in minimum temperature was occurred in Birjand, Urmia and Bandar Abbas with a jump of 0.6 degrees Celsius. It should be mentioned that, unlike other stations, the Khorramabad (Lorestan Province) and Fasa (Fars Province) had a decreasing trend for minimum temperature. It changed from 10.3 to 8.3 and from 11.8 to 10.2 degrees Celsius in Khorramabad and Fasa, respectively. The results showed that the commencement of maximum temperature jump for most of the weather stations happened in 1998 with 1.1 degrees’ Celsius change. According to our study, a remarkable decrease in precipitation data was occurred in west and northwest of the country. There was a depletion of 80 to 150 millimeters from 1998 in Tabriz, Sanandaj, Saghez and Kermanshah weather stations during study period (1966-2015). Besides, 25 to 45 millimeters reduction in precipitation was found in south and southeast of the Country which has arid climate including Birjand (South Khorasan Province), Zabol (Sistan and Baluchestan Province) and Kerman. It was revealed that the variations of minimum temperature were larger than maximum temperature which was in agreement with results obtained by Rafati and Karimi, 2018. The results showed that the start of increasing maximum temperature in most of the weather stations was in 1998. It could be due to increasing the global temperature which is in accordance with results found by Steirou and Koutsoyiannis, 2012. The results revealed that about 80 % of precipitation data of weather stations were homogenized. These results were in agreement with results obtained by Hosseinzadeh Talaee et al., 2013. The results indicated that tests of homogenization for minimum and maximum temperatures and precipitation data could use in different climate over the country. Therefore, it could not allocate a single test to a particular climate type. In conclusion, it should be noted that before any analysis pertaining to environmental hazards, the calibration and maintenance of the weather instruments should be carried out periodically. In addition, the metadata and station history for relocation of the weather station should be checked. The relocation can create great changes in meteorological parameters due to elevation, latitude, longitude and land use/land cover differences between two sites.
 
Key Words: Homogeneity tests, Climate Data, Weather Station, Metadata
 
 
 

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Real-time detection of forest fire using NOAA/AVHRR data
Study area :(Kayamaki Wildlife Refuge)
 
Extended Abstract
Introduction
Land and forest fires are one of the most common problems in the world that cause various disturbances in forest and land efficiency. Real-time fire detection is crucial to prevent large-scale casualties. In order to identify early fire in areas where there is a high risk of fire, it is necessary to monitor these areas regularly. Forest monitoring is a technique used to detect fires in the past using traditional techniques such as surveillance, helicopter and aircraft. Today, satellite imagery is one of the most imperative and effective tools for detecting active fires in the world.
Materials and Methods
In this study, NOAA/AVHRR images were used for fire detection and MODIS products were applied for evaluation and validation.
Fire Detection Algorithms
There are several algorithms for detecting fires using satellite imagery. In this study, 3 algorithms of Giglio, extended and IGPP were used. The selection of these algorithms was due to the extensive background research in most of the previous studies that used them and the results of these algorithms, especially the IGPP, were far more than other algorithms.
Giglio Algorithm
Giglio et al., (1999) criticized Arino and Melinott (1993) threshold as too high for certain regions of the world such as tropical rain forests, temperate climates and marshes where the air temperature for small fires (100 m³) is usually between 308 and 314 degrees Kelvin. They believed that the smaller fires were not fully recognized by Arino and Melinott (1993) thresholds. They concluded that in suburban forests 60% of fires had temperatures below 320K of which 70% were in rainforests and 85% happened in the Savanna. Thus, the threshold cannot be applied on a large scale and it is only applicable for a regional scale.
IGBP Algorithm
The IGBP fire detection algorithm is implemented in two steps. The first step is the threshold test in which a pixel in micrometers (11.03 μm) minus the band 4 is greater than 8 degrees Kelvin, the desired pixel being considered as a potential fire pixel. Band 3 (3.9 μm) exceeds 311 K, and band 3 illumination temperature is 3.9.
Developed Algorithm
This algorithm is used to detect small and large fires (both at night and day).
 
Interpretation of the Results
After selecting fire detection algorithms, pre-processing (geometric, radiometric and atmospheric corrections), processing (applying fire relationships and fire formulas for fire detection) and post-processing (evaluating and validating the results), the fires were identified by the fire algorithms (images). Final results of fires identified for 2016 and 2017 (for 4 days) by fire algorithms indicate that fires identified by Giglio algorithm were 22 cases, those by IGPP algorithm were 27 cases and the ones by the developed algorithm were 15 cases. For this reason, the IGPP algorithm can be taken as the most appropriate algorithm in this study for fire detection using satellite imagery.
Evaluation of fires identified through MODIS products
To evaluate identified fires, after recognizing them with relevant algorithms, we used MODIS products for their evaluation (due to the lack of ground data on the days studied for evaluation). MODIS products were obtained from sites where the location of each fire was reported. For the evaluation of identified fires based on fire detection algorithms with MODIS products, 10 fire occurrences were used. The evaluation results express that out of 10 fires only 7 fires were recognized by the algorithms of MODIS products. 5 fire events were identified by Giglio algorithm (from 7 fires), 6 fires from IGBP (out of 7 fires), and 3 fire events from 7 extended algorithm were selected as fire pixels.
Comparison of the implications of the fire algorithms
The implications of fire occurrence algorithms indicate that the IGBP algorithm with 6 fires (out of 7 tested fires with error rate of 14% and with the number of fires detected (86%)), Giglio algorithm with 5 fires (out of 7 tested fires, with error rate of 28% and with the number of fires (72%)) and the developed algorithm with 3 fires (out of 7 fires tested with an error rate of 57% and with fire rate of 43%) have been identified. Therefore, it is concluded that the IGBP is the most appropriate algorithm for real-time fire detection, followed by Giglio and the developed algorithm in second and third orders, respectively.
Keywords:Real Time Fire Detection, Fire Algorithms, NOAA/AVHRR, Kiamaki Wildlife Refuge.
 

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The zoning of flood risk potential in the Ghotorchay watershed with ANP and WLC multi-criteria decision making methods
 
 
Extended Abstract
Introduction
Flood is one of the most complex and natural destructive phenomena that have many damage every year. The northwestern region of the country, due to its semi-arid and mountainous climate and thus of high rainfall variability, is one of the areas exposed to destructive floods. Flood risk zoning is an essential tool for flood risk management. Therefore, the purpose of this research was to determine the flood risk zones in the Ghotorchay watershed by using the analytical network process (ANP).
 
Methodology
In this research,, with geographic information system (GIS), satellite images, synoptic station data, analytical network process and the combination of layers, the flood potential of has been modeled in the Ghotorchay watershed. The final map of flood risk based on a combination of factors and climatic and physical elements including land use, geology, vegetation, topography, slope and land capability was prepared. The weight of each criterion was determined by ANP method and used by weighted linear composition (WLC) method for spatial modeling and incorporation of layers.
 
Results
The results of flood risk zoning showed that the Qal layers from geology, slopes of less than 3 precent, land capacity of units 5, 6 and 7, and as well as poor vegetation cover were identified as flood zones. The results obtained from the analytical network process model indicate the fact that part of the watershed is affected by the risk of flooding with the very high potential, which is mainly located in the downstream of watershed. For this reason, the streams of rank 3 and 4 are considered as flood zones and flood guide areas to the downstream areas. Also, river networks of 5 and higher ranks are in the range of floodplains or river coastal and usually have surface and extensive floods.
 
Conclusion
The flood prone areas and providing effective solutions for flood management is one of the main steps in reducing flood damage. Therefore more precise management and control of basins with multiple dams, embedding flood alert systems in flood plain areas and performing basic measures is one of the most urgent measures to prevent, improve and control this natural disaster.
Key words: Analytical network process, Biological protection, Floodplain, Flood risk assessment, Ghotorchay
 

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Abastract
Introduction
Road accidents is the outcome of driver behavior, road condition, vehicle status, and environmental factors. Therefore, identification and assessment of effective parameters on road accidents can be considered as an appropriate way to reduce the accident events, driving violations and increase the road safety. Determining the effects of meteorological factors on the road accident events has gained more attention in recent years.
  The The main objective of this study was to investigate the relationship between the number of road accidents and the meteorological variables in the intercity road of Grmi-Ardabil in the Barzand route.
Methodology:
In this regard, the effects of climatic factors (including rainfall amount, the minimum absolute temperature, and the number of frost days) on the frequency of perilous events were analyzed. The data of accident events (in recent 4 years) were obtained from the trooper department of Ardabil Province along with the meteorological parameters of Germi station through a 11-year period. The statistical tests were performed using R programming software through statistical analysis.
Findings and Discussion:
The results showed that the majority of accidents were occurred in winter season which is in consistent with the frequency of frost days and also corresponded to the absolute minimum temperature. According to the results, the highest significant positive correlation at (R²= 0.43) was observed between the number of injured people and frost days. In addition, the relationship between the absolute minimum temperature and the number of were identified as significant negative correlation.
Conclusion:
As a concluding remark, the poor road conditions caused by climate element can be considered increasing the frequency of accident events. Accordingly, the proper strategies related to behavior change could be
considered in setting the rules and regulations to reduce the accidents and the number of injuries.

Keywords: Climatic hazards, Correlation analysis, Frost days, Minimum absolute temperature, Germi-Ardabil road


 

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Soil erosion is one of the environmental problems that is a threat to natural resources, agriculture and the environment, and in this regard, assessing the temporal and spatial amount of soil erosion has an effective role in management, erosion control and watershed management. The main aim of this study was to estimate soil erosion in Amoqin watershed and its relationship with well-known vegetation-based and topographic-related indices. The meteorological data has been used to determine the rainfall erosivity. The rainfall erosivity index was calculated using the modified Fournier index during the 10-year available recorded rainfall data. The value of LS factor has been calculate using digital elevation model. Meanwhile, C and P factors were determined based on the utilization scheme and condition of the study area. Data were analyzed and processed using ArcMap 10.1, ENVI 5.3, and Excel software. In this study, RUSLE model was used to estimate soil erosion, in GIS environment. According to the results, the amount of factor R in Amoqin watershed varies from 12.32 to 50.52 MJ/ha/h per year. The variation of soil erodibility index (K) over the study area is between 0.25 to 0.42. The amount of LS factor varies between 0.19 and 0.38, which is more in high slopes, especially around the waterways and uplands of the study area. The variation of C factor was estimated to be around -0.18 to 0.4. In general, it can be said that the central part of Amoqin watershed has less C value due to the greater area of agricultural activities and the highest amount is related to western areas, especially southwest areas because existing the rangeland areas. Due to the lack of protective measures in the study area, the amount of factor P was considered as unity for the whole region. The base layers of RUSLE factors were obtained and overlayed in GIS to calculate the soil loss in tons per hectare per year. The map of annual soil loss indicate that the erosion amounts varies between 1.21 to 5.53 tons per hectare per year in different parts of the study area. According to the results, the vegetation factor with a coefficient of determination 0.47% had a significant correlation with soil loss. The stream power index with the coefficient of determination of % 0.07% had the lowest correlation with soil erosion values.

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 Determining Strategies for Improving Environmental Resilience in Gharehshiran Watershed in Ardabil using SOAR Analysis Technique
Ebrahim Asgari - PhD Student of Watershed Science & Engineering, Yazd University, Yazd, Iran. Email: ebrahim.asgari90@yahoo.com
Mahboobeh Noori - PhD Student of Geography & Urban Planning, Yazd University, Yazd, Iran. Email: mnori@stu.yazd.ac.ir
MohammadReza Rezaei - Associate Professor of Geography and Urban Planning, Yazd University, Yazd, Iran. Email: mrezaei@yazd.ac.ir
Raoof Mostafazadeh - Associate Professor Department of Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. Email: raoofmostafazadeh@uma.ac.ir (Corresponding author)

Extended Abstract
Introduction: New approaches of crisis management have changed from the concepts of vulnerability to resilience and emphasize on strengthening the system's ability to deal with the risks of natural disasters. Therfore, the aim of this study was identifying the watershed capabilities of Qarahshiran and crisis management planning with emphasis on environmental resilience.
Methodology: The SOAR analytical technique and expert opinions of 52 experts were used to formulate the strategy, determine the strengths, opportunities, ideals and measurable results. The results of SOAR technique and crisis management prevention and preparedness strategies were compared with the environmental resilience of the field.
Results: Based on the results, reducing direct and indirect flood damage with 51.9% and low amount of soil erosion and water loss with 42.3%, were the most important results of the SOAR model. Out of 15 components of environmental resilience, the performance of 5 components was accepted as significant (α<0.05 confidence level). The evaluation of environmental resilience using one-sample t-test showed that the environmental dimension of resilience (2.67) with a significant level (α=0.003) has a significant that indicates high vulnerability and low resilience.
Conclusion: Considering site selection of watershed management structures, creating more opportunities and using the private sector potentials, and local NGOs will be useful in crisis management. Analysis of watershed resilience components in achieving integrated watershed management, proper knowledge of watershed function, possibility of self-regulation and recovery of balance and acceptance of adaptation to natural hazards, co-design of watershed residents, preparedness and coping with crisis can be more effective over the study area.
Key words: SOAR Model, Strategic Planning, Prevention and Preparedness, Resilience, Gharehshiran Watershed

 

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Changes in the hydrological response due to climatic parameters and human induced activities can be derived from indicators based on the analysis of flow duration curves. The purpose of this research is to determine the flood and the low flow parameters using the flow duration curves. The trend detection technique can be used as a useful tool in deterimining the temporal changes of the different hydro-meteorological parameters. The river gauge stations of the Ardabil province were used for the analysis of high and low flow occurrence in this study. The spatial variations of the flood events can be used as a preliminary guideline for the prioritization of the watershed in the vulnerability assessment and management-oriented measures. Also, the assessment of low flow condition is a useful tool in the allocation of environmental flow allocation and utilization of river surface water resources.
Methodology:
In this research, temporal and spatial changes of Q₁₀, Q₅₀, Q₉₀, Q_90/50 and Lane indices in 31 hydrometric stations of Ardabil province during the period from 1993- 2014 were evaluated. The flow duration curve of each river gauge stations was derived. The flow duration curves also were plotted based on the dimensionless flow divided by the mean discharge and the upstream area of each river gauge station. Also, the temporal variations of the of Q₁₀, Q₅₀, Q₉₀, Q_90/50 and Lane indices were analysed using non-parametric Man Kendall trend test. Then the significant level of upward and downward trend directions were determined. In this study, the results of 5 river gauge stations were presented as example based on the the river flow ranges, which includes low, medium and high river flow discharge (Hajahmadkandi, Nanakaran, Shamsabad, Polesoltani, and Booran).
 
Results:
Based on the results, the trend of Q₁₀ (Flood flow index) was significant at the stations located on the main trunk of the Qarehsou river. Meanwhile the Q₅₀ (average flow index) has a significant decreasing trend in most of the studied river gauge stations. In addition, Q₉₀ and Q_90/50 indices have a significant decreasing trend in most stations. In addition, Q₉₀ and Q_90/50 indices had a significant decrease at (p<0.05) regarding the Lane index as a flood related indicator in the Arbabkandi and Dostbeglo stations, which are affected by the dam construction there is a significant decreasing trend.
Conclusion:
I summary, the values of flood flow index in the upstream rivers of the Ardabil province had a increasing trend.

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Evapotranspiration is one of the main elements of hydrologic cycle. Accurate determination of reference crop potential evapotranspiration (ET₀) is crucial in efficient use of water in irrigation practices. ET₀ can be measured directly by lysimeters or estimated indirectly by many different empirical methods. Direct measurement is cumbersome, needs for more time and costly. Therefore, many investigators used empirical methods instead of direct measurements to estimate ET₀. Nowadays, the FAO-56 Penman Monteith (PMF56) method is known a bench mark for comparing the other empirical methods. For example, in the works of Zare Abyaneh et al. (2016), Biazar et al. (2019), Dinpashoh et al. (2021) and Dinpashoh et al. (2011) PMF56 method was used to estimate ET₀ and comparing the outputs of other empirical methods. Many researchers analyzed trends in ET₀ time series in different sites around the Earth. Among them it can be referred to the works of Sabziparvar et al. (2008), Babamiri & Dinpashoh (2015), Dinpashoh et al. (2021), Dinpashoh  (2026) and Tabari et al. (2013). ET₀ can be affected by many different climatic factors such as maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (RH), wind speed, and actual sunshine hours. Factor analysis (FA) is a multivariate method that reduces data dimensionality. In general, climatic variables have high correlation with each other. On the other hand, these variables affect ET₀. The FA can be used to reduce data dimensionality in which correlated variables converted to few uncorrelated factors.