ORIGINAL_ARTICLE
Coastal Features and Settlement Geomorphic Rules (Case study: Northern Coast of Persian Gulf)
Introduction
Thesettlement space has certain logic and rules. The Persian Gulf was 70 meters higher than today sea level. The result of this uplift is the coastal plains which in phenomenological term is called marine context. It is important for a geomorphologist to understand the geomorphological analysis of the rules of the residence in marine context. This study attempted to raise new concepts in geomorphology based on discourse analysis and a phenomenological interpretation to derive the mathematical logic of residence on the shores of the Persian Gulf. The results obtained by this method show that urban residence in the marine context of Khuzestan follow Bifurcation rule, rural residence in the marine context of Khuzestan follow meandering convex rule, and the coastal residence in the marine context of Bushehr - Bandar Abbas follow frequency.
Materials and methods
The data used in this study includes five factors of climate, temperature, precipitation, relative humidity, pressure and evaporation to prepare land context, data from Delaware, GPCC and NCEP / NCAR of global networks. These were processed in programming environment. For homogeneity of spatial resolution, conventional method of resampling was used. The output of this operation was matrices with monthly intervals and 0.5 × 0.5 separation of geographic latitude. After preparing the data, they were descaled and then standardized layers were prepared using linear relation. The next step was preparation of the results of the matrix difference in ArcMap; raster analysis was conducted by combining layers to obtain the final map. After recognition of two distinct texture using Hillier’s Space Syntax, analysis of urban layout and the arrangement of drainage networks of Karkheh and Karun rivers were extracted to determine the extent of rural civilization. The village layout was determined using river meanders. In the second image, the layout logic of coastal population centers was specified with coastal terraces 5, 10, 25 and 50 meters and the logical arrangement of this population were assessed against them.
Results and discussion
The logic syntax in marine context is associated with sea water level changes in the Persian Gulf and Karkheh and Karun rivers. To explain and extract the syntax logic of residence centers on the marine context, we initially specified the networks of Karun and Karkheh rivers, based on Hillier’s syntax logic. In addition, the organization was compared with residence centers. This comparison showed that urban areas are located only in places where rivers have split. To analyze the behavior of the river in connection with rural residence, meanders of Karkheh and Karun rivers were identified. By contrasting rural residence, it was attempted to explain the space identity of each village location with regard to the fluvial meanders. The results showed that the villages in the marine context are settled along the slopes of meanders convexity. The concavities of the rivers have no remnants of residence. To achieve the coastal context syntax logic, the relationship between population centers and coastal terraces were examined through matching the residence spot layers and marine terraces.
The analysis of terraces 5, 10, 25 and 50 meters has indicated that there is a specific relationship between the number of population centers and their population with the distance of the terraces to the coastline and their arrangement along the marine terraces. As the distance of the population centers from the beach are changed largely in number and size; in other words, spatial syntax logic is consistent with Newton's law of gravity inverse.
Conclusion
From the discussion provided, it can be concluded that marine Context defines the syntax rules of settlements and the mathematical rules of layout logic in the area of Iran.
* The first mathematical rule states that marine areas impose Bifurcation rule on river behaviors (Karkheh and Karun). This indicates that urban residence is formed in the division spot.
* The second identifying rule of rivers in marine area is known as river meander which starts to meandering as they enter into marine context. Each wave has a convex side and a concave side. The convex face of wave creates a special place identity that leads to a social organization.
* The results of marine context indicate another rule for the logic of residence centers’ layout that can be summarized as the frequency- magnitude rule.
https://jphgr.ut.ac.ir/article_69785_71fbd318ddc2dbc79d4802b4f613dd7f.pdf
2018-09-23
407
423
10.22059/jphgr.2018.236004.1007072
Phenomenological
marine terrace
space syntax
Persian Gulf
land-context
Fatemeh
Nematollahi
f.nematollahi@geo.ui.ac.ir
1
PhD Candidate in Physical Geography, Faculty of Geography and Planning, University of Isfahan, Iran
AUTHOR
Mohammad Hossain
Ramesht
mh.raamesht@gmail.com
2
Professor of Physical Geography, Faculty of Geography and Planning, University of Isfahan, Iran
LEAD_AUTHOR
Seyed Ali
Almodaresi
almodaresi@gmail.com
3
Associate Professor of RS & GIS, Faculty of Engineering, Islamic Azad University of Yazd, Iran
AUTHOR
پورمند، ح.؛ محمودی، ه. و رنجآزمای آذری، م. (1389). مفهوم مکان و تصویر ذهنی و مراتب آن در شهرسازی از دیدگاه کریستین نوربری شولتز در رویکرد پدیدارشناسی، نشریة مدیریت شهری، 8(۲۶): ۷۹ـ 92.
1
جعفریان، ر. (۱۳۹۱). اطلس شیعه، سازمان جغرافیایی نیروهای مسلح، تهران.
2
جهاد سازندگی بوشهر (1375). طرح اسکان عشایر بوشکان، بخش خاکشناسی.
3
دلسوز، س.؛ محمودی، ط.؛ رامشت، م.ح. و انتظاری، م. (1393). مفهوم زمان و تکنیکهای پیشبینی مخاطرات طبیعی، دانش مخاطرات، 1(۱): ۹۷ـ 109.
4
رامشت، م.ح. (1380). دریاچههای دوران چهارم بستر تبلور و گسترش مدنیت در ایران، تحقیقات جغرافیایی، 16(۱): ۹۰ـ 111.
5
ریکور، پ. و دهشیری، ض. (1378). پدیدارشناسی و هرمنوتیک، مجلة نامة فلسفه، 5: ۲۴ـ56.
6
صالحیزاده، ع. (1390). درآمدی بر تحلیل گفتمان میشل فوکو روشهای تحقیق کیفی، مجلة معرفت فرهنگی اجتماعی، ۲(۷): ۱۱۳ـ142.
7
مسعودینژاد، ر. (1386). مقدمهای بر تئوری Space Syntax، دانشکدة معماری دانشگاه شهید بهشتی، تهران.
8
مرکز آمار ایران (1390). سالنامة آماری ایران، https://www.amar.org.ir/
9
Batty, M. (2017). Space Syntax and Spatial Interaction: Comparisons, Integrations, Applications, CASA, University College London, 90 Tottenham Court Road, London W1T 4TJ, UK, pp. 1-33.
10
Caryl, E. (2014). Holocene Cold Spells Brought Drought and Famine… Sea Levels Were Often Much Higher Than Today- A Short History of the Human Race the Climb Out of the Ice Age, Part 2, http://notrickszone.com/2014.
11
Douglas, J.; Kennett, J. and Kennett, P. (2006). Early State Formation in Southern Mesopotamia: Sea Levels, Shorelines, and Climate Change, California, USA, Journal of Island & Coastal Archaeology, 1: 67-99.
12
Delsoz, S.; Mahmoudi, T.; Ramesht, M.H. and Entezari, M. (2014). Concept of time and forecasting techniques of natural hazards, Hazards Science, 1(1): 97-109.
13
El-Sheimy, N.; Valeo, C. and Habib, A. (2005). Digital Terrain Modeling: Acquisition, Manipulation and Applications, Boston, Landan.
14
Heyvaert, V.M.A. and Baeteman, C. (2007). Holocene sedimentary evolution and palaeocoastlines of the Lower Khuzestan plain (southwest Iran), Marine Geology, 242: 83-108.
15
Hillier, B. (2007). Space is the machine, Cambridge University Press.
16
Hillier, B. and Hanson, J. (1984). The Social Logic of Space, Cambridge, Cambridge University Press.
17
Jafarian, R. (2012). Atlas Shiite, National Geography Organization of Iran, Tehran.
18
Jihad of Construction Bushehr (1996). Project Nomads Bushkan, Soil section.
19
Kennett, Douglas J., Kennett, James P. (2006), Early State Formation in Southern Mesopotamia: Sea Levels, Shorelines, and Climate Change, The Journal of Island and Coastal Archaeology, Volume 1, Issue 1, PP 67-99.
20
Lambeck, K. (1996). Shoreline reconstructions for the Persian Gulf since the last glacial maximum, Australia Earth and Planetary Science Letters, 142: 43-57.
21
Masoudinezhad, R. (2007). Introduction to the Theory of Space Syntax, Faculty Architecture
22
Shahid Beheshti University, Tehran.
23
Pourmand, H.; Mahmodi, H. and Ranj Azma Azari, M. (2011). The meaning of "place" and "subjective imagination" in urban studies from the perspective of christen Schultz in phenomenological approach, Urban Management, 26: 79-92.
24
Purser, B.H. (1973). The Persian Gulf-Holocene Carbonate Sedimentation and Diagenesis in a Shallow Epicontinental Sea, Springer-Verlag, New York, Heidelberg· Berlin.
25
Ramesht, M.H. (2001). Quaternary Lakebeds: Landmarks in Iranian Civilization, Geographical Researches, 16(1): 90-111.
26
Ricœur, P. and Dehshiri, Z. (1998). Phenomenology and hermeneutics, Philosophy, 5: 24-56.
27
Rose, J.I. (2010). New Light on Human Prehistory in the Persian Gulf Oasis, Current Anthropology, 51(6): 849-883.
28
Salehizadeh, A. (2011). Introduction to Michel Foucault Discourse Analysis, qualitative research methods, Ma'rifat-i Farhangi Ejtemaii, 2(3): 113-142.
29
Statistical Center of Iran (2011). Iran Statistical Yearbook, https://www.amar.org.ir/.
30
Suplee, C. (1998). Untangling the Science of Climate, National Geographic, Journal of the National Geographic Society, Washington DC, 193(5): 44-71.
31
ORIGINAL_ARTICLE
Estimation of Evaporation from the Surface of the Caspian Sea and its Temporal and Spatial Analysis
Introduction
Increase in the population of the Caspian Sea, particularly in its coastal zones, will force the governments to use water in the years ahead to gain water from the sea. Therefore, the study of seawater fluctuations influencing the sea ecosystem and the changes resulting from these perceptions will be necessary to prevent serious damage to this environment. In order to investigate the fluctuations in lake water, we need to calculate their water balance, to estimate inputs and outputs, and to determine the level of seawater. One of the most important outguns is evaporation. Calculating evaporation from the lakes is carried out in a variety of ways, with different results. The most accurate estimation of evaporation shows the highest accuracy in estimating the sea level. Thus, the aim of this study is to identify the most accurate and easiest method for estimating evaporation from the Caspian Sea water surface based on available data.
Materials and methods
The data used in this study were collected from three data centers on a daily basis: the Russian Academy of Sciences, the Princeton University of Hydrology, and the NCEP / NCAR database center in 1982-2010. The data were initially reviewed, verified and synchronized. To do this, the data from 1982 to 1998 were selected and the evaporation rate of water was estimated by balance method, Hefner, SHahtin, Meyer, US Bureau of Civil Engineering, Marciano and Ivanov. It should be noted that as in the method, discharge the output water into the Gulf of Kara Bogaz is extracted from the equation of the water balance, in order to compare the evaporation rate with the selected methods, the accuracy of the sampling is decreased. In all these methods, the Gulf of Kara Bogaz is reduced from the Caspian Sea level. Assuming that the water balance method is the most accurate estimation for evaporation, the other methods were compared with. The method that had the least difference in estimation with this method was selected as the optimal method. Then, with the selected method, the evaporation rate was estimated in 1982-2010. The evaporation zonation of the sea level was calculated and plotted on monthly, seasonal and yearly maps.
Results and discussion
The findings of the research showed that Meyer's method in comparison with other selected methods in this study has the closest distance and the highest correlation with the balance method. The amount of annual evaporation by Meyer method in different parts of the Caspian Sea is not the same and varies from 688 mm to 1769 mm from north to south. However, the average of the total body weight is from the body. In the Caspian Sea, during the cold season, the least evaporation rate occurs in the northern part and the most in the southern part of the Gorgan Bay.
Conclusion
According to the results of the base method (balance method), the results showed that Meyer's method has more potential than the other selected methods for estimating evaporation from the Caspian Sea. The methods main relying on climatology element, such as temperature (USBR method), we cannot accurately estimate the evaporation, as a complex and multi-dimensional phenomenon, in the Caspian Sea. On the other hand, among the selected methods, when an influential element such as wind speed can be studied only at one level (the Hefner and Marciano method) alone, we cannot provide an accurate estimate of evaporation changes from the Caspian Sea. Finally, it can be said that among different methods, any method that can check the relationship between at least three factors of wind speed, temperature and water vapor pressure on two levels can provide a better and more realistic picture of evaporation changes from the Caspian Sea level.
https://jphgr.ut.ac.ir/article_69786_c98ed74dd638bbe8603e08b137c68281.pdf
2018-09-23
425
441
10.22059/jphgr.2018.240918.1007111
Evaporation
fluctuation of water level
gridded data
Caspian Sea
Akbar
Zahraei
a.zahraei65@gmail.com
1
PhD Student in Climatology, Faculty of Geographical Sciences, University of Isfahan, Esfahan, Iran
AUTHOR
Javad
Khoshhal Dastjerdi
javadkhoshhal@yahoo.com
2
Associate Professor of Climatology, Faculty of Geographical Sciences, University of Isfahan, Esfahan, Iran
LEAD_AUTHOR
Abdolazim
Ghanghermeh
a_ghangherme@yahoo.com
3
Assistant professor of Geography, Faculty of Human Sciences, Golestan University, Gorgan, Iran
AUTHOR
اقتصادی، ش. و زاهدی، ر. (1390). مطالعة عوامل تأثیرگذار بر نوسانات تراز آب خزر جنوبی، مجلة علوم و فنون دریایی، 10(3): 4ـ13.
1
ترابی آزاد، م. محسنی آراسته، ا. سلامی ابیانه، ر و داریوش منصوری (1389). مطالعۀ تبخیر در خلیج فارس بر اساس یک مدل برهمکنش هوا-دریا، فصلنامۀ علوم و تکنولوژی محیط زیست، 2.
2
وشحال دستجردی، ج. (1376). تحلیل و ارائة مدلهای سینوپتیک کلیماتولوژی برای بارشهای بیش از صد میلیمتر در سواحل جنوبی دریای خزر، رسالة دکتری، دانشگاه شهید مدرس.
3
شمسی، ع. (1379). تبخیر و تبادل حرارتی دریای خزر، مرکز مطالعات و تحقیقات منابع آب دریای خزر، وزارت نیرو.
4
صباغ یزدی، س. و مؤمنی هروی، ع. (1389). اندرکنش تأثیرات تبخیر، بارش، و ورودی رودخانهها در مدلسازی حجم محدود جریانهای افقی روی بستر سهبُعدی دریای خزر، فصلنامة اقیانوسشناسی، 1: 65ـ76.
5
علیزاده، ا. (1382). مبانی هیدرولوژی کاربردی، چ16، انتشارات امام رضا.
6
Allahdadi, MN.; Chegini, V.; Fotouhi, N. and Golshani, AA. (2004). Wave Modeling and Hindcast of the Caspian Sea, Conference: Conference: 6th International Conference on Coasts, Ports, and Marine Structures January 2004, Tehran, IRAN,
7
Alizadeh, A. (2006). Principles of applied hydrology, 16th edition, Imam Reza pub.
8
Chow, V.T. (1964). Handbook of Applied Hydrology, McGraw-Hill Book Company, New York,
9
Dalton, J. (1802). Experimental essays on evaporation, Manchester Literary Philosophical Society Proceedings, 5: 536-602.
10
Delclaux, F.; Coudrain, A. and Condom, T. (2007). Evaporation estimation on lake Titicaca: a synthesis review and modeling, Hydrological Processes, 21: 1664-1677.
11
Dos Reis, R.J. and Dias, N.L. (1998). Multi-season lake evaporation: energy-budget estimates and CRLE model assessment with limited meteorological observations, Journal of Hydrology, 208: 135-147.
12
Eghtesadi, Sh. and Zahedi, R. (2011). Investigation of Factors Affecting South Oscillatory Water Fluctuations, Journal of Marine Science and Technology, 10(3).
13
Filimonova, A. and Trubetskova, M. (2005). Calculation of evaporation from the Caspian Sea surface, Stochastic Hydraulics 2005 - 23 and 24 May 2005.
14
Ibrayev, R. A., zsoy, C., Schrum,O., and Sur,H., (2010), Seasonal variability of the Caspian Sea three-dimensional circulation, sea level and air-sea interaction, Ocean Sci311:329.
15
International Environment House (2016). Programme DEWA/GRID-Geneva.
16
Iranian Ports and Maritime Organization (2017). Department of Statistics and Information.
17
Khoshhal Dastjerdi, J. (1997). Analysis and presentation of Synoptic Climatology models for precipitation over 100 mm on the southern shores of the Caspian Sea, Ph.D., Shahid Modarres University.
18
Malekinezhad, H.,(2012), comparative study of climatic parameters affecting evaporation in central and southern coastal areas in Iran, Water resources and wetlands, PP-605:618.
19
Marciano, J.J. and Harbeck, G.E. (1954). Mass-transfer studies. In: USGS (Editor), Water-Loss errors in daily and monthly input data, Hydrological Processes, 11(11): 1465-1473.
20
Meyer, A.F. (1942). Evaporation From Lakes and Reservoirs: A Study Based on Fifth Years, NY, USA.
21
Morton, F.I. (1983b). Operational estimates of lake evaporation, Journal of Hydrology, 66: 77-100.
22
NOAA National Center for Environmental Prediction Reanalysis Information (2016). https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.
23
Panin, G., (2007), Caspian Sea level fluctuations as a consequence of regional climatic change. In: Lozán, l Wissenschaftliche Auswertungen, Hamburg, 384 p.
24
Princeton University, Dept. Civil and Environmental (2016). Terrestrial Hydrology Group, Princeton, NJ 08544 .
25
Qin, B. and Huang, Q. (1998). Evaluation of the climatic change impacts on the inland lake - a case stdy of Lake Qinghai, China, Climatic Change, 39: 695-714.
26
Sabbagh Yazdi, S.R. and Momeni Heravi, A. (2010). Interaction of evapotranspiration, precipitation and river influences in the modeling of the limited volume of horizontal flows on the 3D surface of the Caspian Sea, Oceanographic Quarterly, No. 1.
27
Shamsi, A. (2000). Thermal evaporation and heat exchange in the Caspian Sea, Caspian Water Resources Research and Research Center, Ministry of Energy.
28
Sturrock,A.,(1978), Evaporation and Radiation Measurements at Salton Sea, California, Library of Congress Cataloging in Publication Data, Supt. of Docs. No. : 119.13: 2053.
29
Vallet-Coulomb, C.; Legesse, D.; Gasse, F.; Travi, Y. and Chernet, T. (2001). Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia), Journal of Hydrology, 245: 1-18.
30
Winter, T.C. (1981). Uncertainties in Estimating the Water Balance of Lakes, Water Resources Bulletin, 17(1): 82-115. NY, USA.
31
Xu. C.-Y., and V.P., Singh(1998), Dependence of evaporation on meteorological variables at different time-scales and intercomparison of estimation methods, Hydrological Processes, Hydro. Process, PP- 429:442.
32
ORIGINAL_ARTICLE
Application of Chaos Theory in Modeling and Analysis of River Discharge under Different Time Scales (Case Study: Karun River)
Introduction
One of the main issues in hydrology and water resources is investigation of river flow. Due to innovations and capabilities of the chaos theory, nowadays, chaos analyses are used to analyze river-flow time series. Since investigation of the presence of different characteristics at different time scales in rivers is one of the main challenges of hydrology in recent years, the aim of this paper is to study the behavior of river flow at different time scales. The behavior of river discharge can be studied precisely by applying nonlinear and chaotic analyses. The chaos theory, as the foundation of nonlinear dynamic systems has created great changes in understanding and expressing the mode of different phenomena in recent decades. This theory deals with the study of systems that at first glance may seem irregular; but in fact they are governed by clear rules. Such systems are very sensitive to primary conditions, so that seemingly minor inputs could have a significant impact on that. Such systems are called chaotic. With regard to recent studies, based on chaos theory for flow discharges, the chaotic or random nature of a system could be identified by using some discriminative indices. Despite chaotic studies conducted on the river discharges, chaotic analysis of flow discharge in Karun River has not been implemented for different time scales.
Materials and methods
In this study, the presence of chaos at daily, monthly and seasonal scales in discharge data of Karun River, Mollasani station, is discussed. Mollasani station is located downstream Ghir barrage (where, Dez, Gargar and Shotait River join together) and upstream Mollasani city. Daily, monthly and seasonal flow discharge data in Mollasani station (1967 to 2011) are used. Four nonlinear dynamic methods were used: 1) phase space reconstruction, 2) correlation dimension method, 3) largest Lyapunov exponent, and 4) spectral power. The state (phase) space is a useful tool for studying dynamic systems. According to this concept, a dynamic system can be described by means of a state space diagram. Each dynamic system consists of differential equations with partial derivatives. To determine these equations and their type, the embedding dimension and time delay parameter must be determined. The delay time could be obtained from the method of assessment of correlation function (ACF) or average mutual information (AMI). In this study, the average mutual information is used to estimate delay time of the dynamic system. In this method, time of first minimum occurrence in the average mutual information function is selected as the appropriate delay time. The embedding dimension is obtained from the false nearest neighbor (FNN) method. This algorithm provided information concerning optimal embedding dimension for the dynamic system.
Results and discussion
The results showed that the daily times for daily, monthly and seasonal data are 97, 2 and 1, respectively, and the optimal embedding dimensions are 9, 6 and 2, respectively. To determine chaotic nature of the system, correlation coefficient was calculated. The correlation dimension at the monthly scales, due to saturation of the diagram, is obtained as 2.704. Therefore, Karun River system is chaotic at this scale. But at the daily and seasonal scales, the diagram's trend was ascending and as a result, the river discharge is random. Another indicative criterion of the chaotic system is the largest Lyapunov exponent. The behavior could be measured in each dimension by using the Lyapunov exponent. Presence of positive Lyapunov exponent is an important indicator of the chaotic system. In this study, elongation factors and largest Lyapunov exponent are calculated on the basis of Rosenstein's method. Taking the value of optimal embedding dimension as m, the value of this exponent can be calculated. In the absence of the optimal embedding dimension, this parameter is predicted based on different m values. At monthly tile scale, the largest Lyapunov exponent was positive (0.0093). The extent of band width at monthly scale is another proof of chaotic nature of this river's discharge. The chaotic nature of the discharge data can also be calculated by power range. These methods can estimate the chaotic or non-chaotic behavior and cannot estimate the complexity of data.
Conclusion
At daily and seasonal scales, according to correlation dimension, the river discharge is random (non-chaotic). But, flow is chaotic at the monthly scale. It seems that the geographical location of Mollasani station may affect the chaotic or randomness of Karun River's discharge.
https://jphgr.ut.ac.ir/article_69787_1549b95d7d3657c6374ef17c2a29851d.pdf
2018-09-23
443
457
10.22059/jphgr.2018.234491.1007061
chaos theory
correlation dimension
largest Lyapunov exponent
Prediction
time scale
Fatemeh
Adab
f.adab.1312@gmail.com
1
MA in Civil Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
Hojjat
Karami
hkarami@semnan.ac.ir
2
Assistant Professor of Civil Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
LEAD_AUTHOR
Seyyed Farhad
Mousavi
fmousavi@semnan.ac.ir
3
Professor of Civil Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
Saeid
Farzin
saeed.farzin@semnan.ac.ir
4
Assistant Professor of Civil Engineering, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
اعلمی، م.ت. و ملکانی، ل. (1392). بازسازی فضای حالت و بٌعد فرکتالی جریان رودخانه با استفاده از زمان تأخیر و بٌعد محاط، نشریة مهندسی عمران و محیط زیست، 43(70): 15-21.
1
پری زنگنه، م.: عطایی، م. و معلم، پ. (1389). بازسازی فضای حالت سریهای زمانی آشوبی با استفاده از یک روش هوشمند، فصلنامة پژوهش در فناوری برق، 1(3): 3-10.
2
جانی، ر.؛ قربانی، م. و شمسایی، ا. (1394). تحلیل بارش ماهانة بندرانزلی با استفاده از نظریة آشوب در شرایط تغییر اقلیم، مجلةپژوهش آب ایران، 9(1): 29-39.
3
شقاقیان، م.ر. و طالب بیدختی، ن. (1388). بررسی وجود آشوب در جریان رود در مقیاسهای زمانی گوناگون، نشریةمهندسی منابع آب، 2(3): 1-8.
4
طباطبایی، م.ر.؛ شاهدی، ک. و سلیمانی، ک. (1392). مدل شبکة عصبی مصنوعی برآورد غلظت رسوب معلق رودخانهای به کمک تصاویر سنجندة مودیس (مطالعة موردی ایستگاه هیدرومتری ملّاثانی- رودخانة کارون)،نشریةآب و خاک، 27: 193-204.
5
انیسحسینی، م. و ذاکر مشفق، م. (1393).تحلیل و پیشبینی جریان رودخانة کشکان با استفاده از نظریة آشوب، مجلة هیدرولیک، 8(3): 45-61.
6
ادب، ف. (1395). شبیهسازی و تحلیل دبی جریان رودخانههای کارون و دز با استفاده از نظریة آشوب، پایاننامة کارشناسی ارشد مهندسی و مدیریت منابع آب، دانشگاه سمنان.
7
فهیمفرد، س.؛ شمسایی، ا.؛ فتاحی، م. و فرزین، س. (1394). بررسی تأثیر سد بر الگوی آشوبی انتقال بار معلق رود (مطالعة موردی: سد کرج)، مجلة مهندسی منابع آب، 8: 89-100.
8
مرادیزاده کرمانی، ف. (1389). تخمین جریان رودخانه با استفاده از نظریة آشوب و برنامهریزی ژنتیک در مقیاسهای زمانی مختلف، پایاننامة کارشناسی ارشد، دانشگاه تبریز.
9
هاشمی گلپایگانی، م. (1388). آشوب و کاربردهای آن در مهندسی، تهران: امیرکبیر.
10
Abrabanel, H. (1996). Analysis of Observed Chaotic Data, Springer-Verlag, New York.
11
Adab, F. (2016). Simulation and Analysis of River Flow of Karun and Dez Rivers Using Chaos Theory, MSc. Thesis, Semnan University.
12
Alami, M.T. and Malekani, L. (2014). Phase Space Reconstruction and Fractal Dimension Using of Delay Time and Embedding Dimension, Journal of Civil and Environmental Engineering, 43(1): 15-21 (Text in Persian).
13
Banks, J.; Dragan, V. and Jones, A. (2003). Chaos: A Mathematical Introduction, Cambridge University Press.
14
Elshorbagy, A.; Simonovic, S. and Paun, U.S. (2002). Estimation of Missing Streamflow Data Using Principle of Chaos Theory, Journal of Hydrology, 255: 123-133.
15
Farzier, C. and Kockelman, K. (2004). Chaos Theory and Transportation System: An Instructive Example, Proc. of 83rd Annual Meeting of the Transportation Research Board, Washington D.C., USA.
16
Ghorbani, M.A.; Kisi, O. and Alinezhad, M.A. (2010). Probe Into the Chaotic Nature of Daily Streamflow Time Series by Correlation Dimension and Largest Lyapunov Methods, Applied Mathematical Modeling, 34: 4050-4057.
17
Hashemi Golpayegani, M. (2009). Chaos and Its Applications in Engineering, Amir Kabir University Press.
18
Jani, R.; Ghorbani, M. and Shamsaei, A. (2015). Analysis of Monthly Rainfall in the Bandar Anzali Using Chaos Theory under Climate Change Conditions, Iranian Water Research Journal, 9(1): 29-39 (Text I Persian).
19
Kockak, K.; Bali, A. and Bektasoglu, B. (2007). Prediction of Monthly Flows by Using Chaotic Approach, International Congress on River Basin Management, Antalya Turkey, pp. 553-559.
20
Lange, H. (2003). Time Series Analysis of Ecosystem Variables with Complexity Measures, InterJournal for Complex Systems, 250: 1-9.
21
Moradizadeh Kermani, F. (2010). Estimation of River Flow Using Chaos Theory and Genetic Programming in Different Time Scales. MSc. Thesis, Tabriz University.
22
Ott, E. (2002). Chaos in Dynamical Systems, Camdridge University Press, New York.
23
Pari Zanganeh, M.; Ataei, M. and Moallem, P. (2010). Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method, Journal of Transactions of Electrical Technology, 1(3): 3-10.
24
Regonda, S.K.; Sivakumar, B. and Jain, A. (2004). Temporal Scaling in River Flow: Can It be Chaotic?, Hydrological Sciences Journal, 49(3): 373-385.
25
Fahimfard, S.; Shamsaei, A.; Fattahi, M. and Farzin, S. (2015). Investigation of the Effect of Dam on Chaotic Pattern of Suspended Load Transport (Case Study: Karaj Dam), Journal of Water Resources Engineering, 8: 89-100 (Text in Persian).
26
Shaghaghian, M.R. and Talebbeydokhti, N. (2009). Investigation of Chaos in River Flow at Different Time Scales, Water Resources Engineering, 2(3): 1-8 (Text in Persian).
27
Sivakumar, B. (2001). Rainfall dynamics at different temporal scales: A chaotic perspective, Hydrology and Earth System Sciences, 5(4): 645-652.
28
Shang, P.; Li, X. and Kamae, S. (2005). Chaotic Analysis of Traffic Time Series, Chaos, Solitons and Fractals, 25: 121-128.
29
Sivakumar, B. (2009). Nonlinear dynamics and chaos in hydrologic system: Latest developments and a look forward, Stochastic Environmental Research and Risk Assessment, 23: 1027-1036.
30
Tabatabaei, M.R.; Shahedi, K. and Soleymani, K. (2013). Artificial Neural Network Model of Estimating Suspended Solids Concentration of River Using Modis Images (Case Study: Mollasani Hydrometric Station- Karun River), Journal of Soil and Water, 27: 193-204 (Text in Persian).
31
Yabin, S. and Chi, D. (2014). Improving Numerical Forecast Accuracy with Ensemble Kalman Filter and Chaos Theory, Journal of Hydrology, 512: 540-548.
32
ORIGINAL_ARTICLE
Sensitivity of Form and Evolutionary Parameters of Meanders to Small Rivers Dynamics, (Case Study: Ghere – Sou River in Kermanshah)
Introduction
Shield (2000), using some geometric activity parameters, explains how reservoir can be effective on downstream river channel migration. After that, Magdaleno and Fernández-Yuste (2011) recognized that these parameters may complement the classical form parameters and represent the real functioning of the river corridor, in geomorphological analyses of meander dynamics. However, the effectiveness of these indices is not clear on other channels where meandering is not very developed and it is not clear if or not the geometrical parameters can indicate the type of functional and dynamics of this type of rivers.
Materials and methods
We have selected an area about 52 km wide along the total length, 219 km, of the Ghere- Sou River as the study area; this is because of location of Kermanshah Plain on a fault line that allows smaller changes. The study of the sinuosity of the river during the 54 years (1955 to 2009) shows that the number of meanders has been increased and decreased constantly. The meanders are included just a quarter of windings, at this study.
To study the dynamics of sinuosity, we used form parameters such as the radius of curvature, wavelength, amplitude, meander length, and evolutionary parameter such as bankfull width, magnitude of channel lateral migration, area occupied by the active channel and channel activity coefficient to determine the evolution of the meander belt in the central sector of Ghere-Sou River.
At first, aerial photos from 1954 and 1967 (black and white; approximate scale 1:55,000 and 1:20,000) and satellite images of IRS 2004 and 2009 (colour; with 2 m. resolution) were digitized and the factors was measured in ArcGIS. Using the Kolmogorov-Smirnov normality test for three form factors (wavelength and the radius of curvature and amplitude), Friedman tests were examined for abnormal data and ANOVA test for normal data. Finally, the spatial distribution for the morphology parameters was analyzed in order to determine if they showed a change downstream. Comparing the form and geomorphic activity parameters elucidated which groups of parameters are powerful to show the dynamics of river.
Results and discussion
Ghere-Sou River has curvatures as sinuosity and meanders at 81 points, totally. The curvatures numbered 1 to 81 from up to downstream. Comparing these points at four years (1955, 1967, 2004 and 2009) showed that sinuosity has decreased at some points and it has increased at some other points. Thus, we can see that the number of arcs has been changed from 77 to 79 and then 77 and finally to 65. The sinuosity characteristics of arcs have been changed through these periods. However, three parameters including radius of curvature, wavelength, and amplitude have suffered from very little fluctuations. The results of Friedman and ANOVA show that there is not significant difference between them. However, other parameters including meander length, bankfull width, magnitude of channel lateral migration, the area occupied by the active channel and channel activity coefficient have indicated a progressive trend from 2005 to 2009.
A glance at the average bankfull width of river in different years show that this factor was about 108 m in 1955, 95 m in 1967, and 77 m in 2004 and finally reached to 89 m in 2009. This means that river has been more dynamic during last 5 years and it has added 11 meters to its width on average. The same is also true about average of lateral migration of river, so that the first until 2.5, 0.92, and 3.5 m per year, respectively. The total area occupied by means of channel activity had increased in the period. Channel activity coefficient is reached from 2.51 at the first period to 0.82 at the second period and finally to 2.76 at the third period. Therefore, the river is desired to have a state of static equilibrium at the first and second period and now try to have a state of dynamic equilibrium. The evolutionary changes are conducted in GIS and cause to promote the analysis capacity.
The river has been divided into 11 reaches in the two Google earth images in 2005 and 2015 and they were union together at the ArcMap. The results illustrate that the river has had up to 70% overlapping in the fifth reach. However, the changes of river have become more apparent at sixth reach while going to be channelized. This situation is to be the same downstream because of river balancing nature.
Conclusion
This study about the dynamics of Gher-Sou River elucidated that only the form parameters (wavelength, amplitude, curve radius) don’t explain evolutionary characteristics and we need some morphological parameters such as magnitude of channel lateral migration, the area occupied by the active channel and channel activity coefficient. Some compositional parameters such as radius of curvature to the average bankfull width can also show these changes. These results confirmed by Magdaleno and Fernández-Yuste (2011) highlighted dynamic equilibrium of Ghare-Sou River in Kermanshah plain since 2004. The dynamics are coincided with urbanization and the expansion of activities and projects implementation.
Further study on the parameters may be a cause to produce more accurate parameters in relation to evolutionary characteristics; for example, the remaining of active channels overlapping may provide better results in some cases. The parameter is obtained from overlaying of two periods and calculation of non-intersected area. Using GIS, it can be obtained by combination of different layers of the area.
https://jphgr.ut.ac.ir/article_69788_a771ee0acf2a72508dec2f26d47ef3e7.pdf
2018-09-23
459
471
10.22059/jphgr.2018.204721.1006850
river dynamics
Ghare-Sou River
geomorphology
channel change
meander
Iraj
Jabbari
ir_jabbari@yahoo.com
1
Associate Professor of Geography, Faculty of Literature, Razi University, Iran
LEAD_AUTHOR
Tahere
Rahimi Javid
rahimi.javid87@yahoo.com
2
MA in Geomorphology, Faculty of Literature, Razi University, Iran
AUTHOR
بیاتی خطیبی، م. (1391). بررسی رفتار پیچانرودها در دشتهای سیلابی نواحی نیمهخشک، مطالعة موردی: دشتهای سیلابی دامنههای جنوبشرقی کوهستان سهند (رودخانة شور و قرهآغاج)، فصلنامة تحقیقات جغرافیایی؛ س27، ش3، 18448ـ 18472.
1
رضایی مقدم، م.ح. و خوشدل، ک. (1388). بررسی پیچوخم های مئاندر اهرچای در محدودة دشت ازو مدل ورزقان، جغرافیا و برنامه ریزی محیطی، ش7: 40-58.
2
علایی طالقانی، م.؛ حاصلی، ف. و احمدی ملاوردی، م. (1392). ارزیابی نقش انسان در فرسایش کنارهای وگسترش جانبی مئاندرهای رودخانة گاماسیاب در دشت بیستون، مجلة جغرافیا و پایداری محیط، 6: 107-120.
3
مقصودی، م.؛ شرفی، س. و مقامی، ی. (1389). روند تغییرات الگوی مورفولوژیکی رودخانة خرمآباد با استفاده از Auto Cad و GIS، RS، مجلة برنامهریزی و آمایش فضا (مدرس علوم انسانی)، 14 (3): 275-294.
4
یمانی، م. و شرفی، س. (1391). ژئومورفولوژی و عوامل مؤثر در فرسایش کناری رودخانه هررود در استان لرستان، مجلة جغرافیا و برنامهریزی، 23(1): 15-32.
5
Addink, E. and Kleinhans, M. (2008). Recognizing meanders to reconstruct river dynamics of the Ganges. In: Hay, G., Blaschke, T., Marceau, D. (Eds.), GEOBIA (Pixels, Objects, Intelligence: Geographic Object Based Image Analysis for the 21st Century). Vol. 48, part 4/C1 of The international archives of the photogrammetry, remote sensing and spatial information sciences. Int. Soc. for Photogrammetry and Remote Sensing (ISPRS), Calgary, Canada.
6
Alaee Taleghani, M.; Haseli, F. and Ahmadi Malaverdi, A. (2013). Assessing Human Role in Lateral Erosion and Lateral Extension of Gamasiab River in Bistoon Plain, Journal of Geograhy and Environental Sustainbility, 6: 102-107.
7
Bayati Khatibi, M. (2012). Investigation the behavior of meanders on flood plains in semi-arid regions, Case stady: Flood plains on North East of Sahand Mt., Geographical Research, 3 (136): 182-212.
8
Brice, J.C. (1977). Air Photo Interpretation of the Form and Behavior of Alluvial Rivers. Final Report to the U.S. Army Research Office—Durham. Washington University, St. Louis, MO. 10 pp.
9
Gbris, G. and Nàdor, A. (2007). Long-term fluvial archives in Hungary: response of the Danube and Tisza rivers to tectonic movements and climatic changes during the. Quaternary: a review and new synthesis, Quaternary Science Reviews, 26: 2758-2782.
10
Gurnell, A.M.; Downward, S.R. and Jones, R. (1994). Channel planform change on the River Dee meanders, 1876–1992, Regulated Rivers: Research & Management, 9(4): 187-204.
11
Güneralp, I.; Abad, J.D.; Zolezzi, G. and Hooke, J. (2012). Advances and challenges in meandering channels research, Geomorphology, 163-164: 1-9.
12
Hooke, J.M. (1987). Changes in meander morphology. In: International Geomorphology, Part 1, ed. V. Gardiner, pp. 591-609. Chichester, UK: John Wiley and Sons, Ltd.
13
Hooke, J.M. (2007). Spatial variability, mechanisms and propagation of change in an active meandering river, Geomorphology, 84(3-4): 277-296.
14
Howard, A.D. (1992). Modeling channel migration and floodplain sedimentation in meandering streams. In: Carling, P.A., Petts, G.E. (Eds.), Lowland Floodplain Rivers: Geomorphological Perspectives, John Wiley and Sons, Chichester, UK.
15
Lagasse, P.F.; Zevenbergen, L.W.; Spitz, W.J. and Thorne, C.R. (2004). Methodology for Predicting Channel Migration, NCHRP Web-Only Document 67 (Project 24-16), National Cooperative Highway Research Program, Transportation Research Board, Washington, D.C.
16
Latapie, A.; Gamenon, B.; Rodrigues, S.; Paquier, A.; Bouchard, J.P. and Moatar, F. (2014). Assessing channel response of a long river influenced by human disturbancs, Catena, 121: 1-12.
17
Lutgens, F.K. and Tarbuck, E.J. (1995). Essentials of Geology, Prentice Hall, Englewood Cliffs, NJ.
18
MacDonald, T.E.; Parker, G. and Leuthe, D.P. (1991). Inventory and analysis of stream meander problems in Minnesota, St Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN, USA: 37 pp.
19
Magdaleno, F. and Fernàndez-Yuste, J.A. (2011a). Hydrogeomorphological alteration of a large Mediterranean river: relative role of high and low floes on the evolution of riparian forests and channel morphology, River Research and Applications, 27(3): 374-387.
20
Magdaleno, F. and Fernàndez-Yuste, J.A. (2011b). Meander dynamics in a changing river corridor, Geomorphology, 130: 197-207.
21
Maghsoodi, M.; Sherefi, S. and Meghami, Y. (2010). The Trend of Morphologic pattern Changes in Khoremabad River using RS, Auto Cad and GIS, The Journal of Spatial Planning, 14(3): 275-294.
22
Nanson, G.C. and Hickin, E.J. (1986). A statistical analysis of bank erosion and channel migration in Western Canada, Geological Society of America Bulletin, 97(8): 497-504.
23
Nanson, G.C. and Hickin, E.J. (1983). Channel migration and incision on the Beatton River, Journal of Hydraulic Engineering, ASCE, 109(3): 327-337.
24
Neill, C.R. (1987). Sediment balance considerations linking long-term transport and channel processes, In: Thorne, C.R., Bathurst, J.C., Hey, R.D. (Eds.), Sediment Transport in Gravel-bed Rivers, Wiley, New York, pp. 225-242.
25
Po-Hug, Y. and Namgyu, P. (2009). Maximum migration distance of meander channel in sand using hyperbolic function approach, Journal of Hydraulic Engineering, ASCE, 135(8): 629-639.
26
Rezaie Moghadem, M.H. and Khoshdel, K. (2019). The Study of Meanders Curvatures of Aher-Chai in Vezerghan plain Area, Geography and environmental planning, 7: 40- 58.
27
Richard, G.A.; Julien, P.Y. and Baird, D.C. (2005). Case study: modeling the lateral mobility of the Rio Grande below Cochiti Dam, New Mexico, Journal of Hydraulic Engineering, ASCE, 131 (11): 931-941.
28
Shields Jr, F.D.; Simon, A. and Stefeen, L.J. (2000). Reservoir effects on downstream river channel migration, Environmental Conservation, 27(1): 54-66 .
29
Yamani, M. and Sharafi, S. (2011). Geomorphology and effective factors on lateral erosion in Hor Rood River, Lorestan province, Geomorphology and environmental planning, 23(1): 15-23.
30
Yousefi, S.; Pourghasemi. H.R.; Hooke, J. and Navartil, O. (2016). Changes in morphometric meander parameters identified on the Karoon River, Iran, using remote sensing data, Geomorphology, 271: 55-64.
31
ORIGINAL_ARTICLE
Spectral Analysis of Spatial Relationship between Surface Wind Speed (SWS) and Sea Surface Temperature (SST) in Oman Sea
Introduction
Surface wind speed (SWS) and sea surface temperature (SST) are interacting as climatic, atmospheric and oceanic parameters. In such a way, variations in the SST are considered to be the factors in wind speed values and in the future weather forecasting model, monitoring SWS changes plays an important role in identifying the SST heating pattern. Over the past few years, wind speed has indicated a clear decrease in many areas. When wind speed decreases, urban air pollution does not stagnant. On the other hand, changes in SST can bring about various effects on marine environments. One of the most important effects in long term is reduction of the pattern of ocean cycles, which brings nutrients from the depths to the sea surface. This can carry dissolved oxygen from the surface into the deep ocean. Furthermore, due to the interaction between the atmosphere and oceans, SST can bring about dramatic effects on global climate. An important point in the SWS and SST studies is that simultaneous examination of these two parameters makes it possible to study the interactions between atmosphere and ocean. Accordingly, the present study aims to investigate the relationship between surface wind speed and sea surface temperatures in the Gulf of Oman using one of the most important instruments of spatial statistics (spatial autocorrelation techniques) from 2003 to 2015.
Study area
The Gulf of Oman is a watershed located in the northwest part of Arabian Sea and the Indian Ocean and in east part of the Strait of Hormuz and the Persian Gulf. Through this sea, the Persian Gulf is connected to the Indian Ocean. The gulf is relatively deep and has a depth of 3550 meters, which the depth is reduced in the west and reaches 72 meters near the Strait of Hormuz. Due to the passage of Tropic of Cancer from this watershed zone, this gulf is one of the warmest seas in Southwest Asia. The orientation of surface currents is along the coast of the Gulf of Oman from north-west to south-east during the winter, but during the winter general currents are from the Oman Sea towards the Persian Gulf and reverse in the summer. Iran and Pakistan are located in the northern areas of the Gulf and Oman, and a small part of the UAE in the south. The Gulf of Oman is located at coordinates 22°-27° of northern latitude and 56°-61° of Eastern longitude.
Materials and methods
According to Anselin, the place has two kinds of effects, spatial dependence and spatial heterogeneity. The first is the spatial correlation or spatial continuity that follows directly the first Law of Geography, Tobler law. This means that the values close to each other are more similar to each other and this leads to spatial aggregation. The second is the spatial impact belonging to regional or spatial differences that follow the inherent uniqueness of each place. Determination of the degree of scattering or clustering of complications in space is possible using Global Spatial Moran Autocorrelation, Global Moran’s I. In fact, this index is intended to describe the spatial characteristics of a variable in the whole region, and it can be used to determine the mean space difference between all spatial cells and their adjacent cells. In global Moran index, in addition to application of the arrangement of complications, remarkable attention is also paid to the characteristics of the complications and the status of spatial autocorrelation based on the location and the internal values of the complications. There are various spatial techniques to represent the statistical distribution of phenomena in space; one of the most authentic indices derived from the Anselin Local Moran's I. Using weighted spatial features and with the aid of this statistic, we can find points with small or high distribution represented in clusters or values with high difference (outliers). The Anselin local Moran’I explains the pattern of a spatial correlation of a spatial parameter in neighborhoods. This index was developed by Anselin in 1995 with the aim of identifying local sites and proposing effective individual sites in spatial links.
Result and discussion
In order to evaluate the relationship between SWS and SST and determine the type of spatial distribution, two variable Global Moran is calculated for monthly and yearly periods. The results of this study in monthly periods indicate that there is a positive relationship among evaluated parameters during cold months but, the relationship is negative and reverse during warm months. Previous surveys documented that from January, as a cold month of year, toward warm months the relationship become more negative and reverse. The most negative form of that is related to July. Then, with the start of cold season, relation of parameters is changed again to positive and direct, as the most positive case occurs on January. The values of two variables of Global Moran between SWS and SST which is examined for a period of 13 years show a negative number and represent a reverse relationship between them. In addition, Moran index values follow a decreasing and negative pattern over time. The surface wind speed and surface temperature of Gulf of Oman is being more reverse. Analysis of local Moran shows that cold months have the most number of spatial clusters (High-High and Low-Low) and warm months experience the most number of spatial outliers (High-Low and Low-High). It could be concluded that the greater number of spatial clusters in comparison to spatial outliers may lead to positive and direct relationship between surface wind speed statistics with sea surface temperature. The negative autocorrelation and reverse relation of these parameters are due to the greater number of spatial outliers. At the next step, the annual changes of High-High and Low-Low clusters is evaluated and it was found that spatial clusters formed in the Gulf of Oman during annual period were very small and the number of spatial outliers formed was much higher. This is consistent with the results indicated by negative values of Global Moran index. Also, there are rise and falls in the number of High-Low outliers during 13 years but in general, the formation of these outliers in the Oman Sea has been declining. On the other hand, changes in the timing of the diagram of Low-High outliers don’t show an increasing or decreasing pattern but closer looks show that from 2009, these outliers are increasing and decreasing by a periodic form. These points tend to increase. By this interpretation, the increasing pattern of negative autocorrelation and reverse relation, which was obtained by Global Moran analysis, could be attributed to Low-High outliers.
Conclusion
It can be concluded that two parameters of surface wind speed and surface sea temperature have a direct and positive relationship during cold months and a reverse and negative relation in warm months. The reason of these phenomena could be related to interaction of the factors such as latent heat flux and humidity changes. The effects of surface evaporation and Manson air masses are likely possible to create this situation. Therefore, it is necessary to study these parameters simultaneously. Annually changes in scales show that surface wind speed is gradually decreasing and sea surface temperature is increasing. It should be mentioned that the sea surface temperature in Oman Sea was evaluated by this technique and found that during a period of 13 years, the temperature variable follows an increasing pattern in this region. According to the results, the effects of climate change and global warming on surface wind speed and sea surface temperature in Oman Sea are very likely and possible and it is needed to continue monitoring of these parameters and the other climatic and oceanic factors which are affected by them.
https://jphgr.ut.ac.ir/article_69789_be0737dd2168f9437aba20b9c37f6355.pdf
2018-09-23
473
489
10.22059/jphgr.2018.245219.1007137
surface wind speed
sea surface temperature
spatial Autocorrelation
arcgis
Oman Sea
Younes
Khosravi
khosravi@znu.ac.ir
1
Assistant Professor of Environmental Science, Environmental Science Research Laboratory, Department of Environmental Science, Faculty of Science, University of Zanjan, Iran
LEAD_AUTHOR
Ali
Bahri
ali.bahri@znu.ac.ir
2
MA in Environmental Science, Department of Environmental Science, Faculty of Science, University of Zanjan, Iran
AUTHOR
Azadeh
Tavakoli
atavakoli@znu.ac.ir
3
Assistant Professor of Environmental Science, Environmental Science Research Laboratory, Department of Environmental Science, Faculty of Science, University of Zanjan, Iran
AUTHOR
اولیازاده، ن. (1389). مطالعة اثرات مونسون بر روی یک جبهه میان مقیاس اقیانوسی (رأسالحد)، پایاننامة کارشناسی ارشد فیزیک دریا، دانشکدة علوم و فنون دریایی، دانشگاه آزاد اسلامی واحد علوم و تحقیقات.
1
ترابی آزاد، م.؛ علیاکبری بیدختی، ع.ع. و صالحیانفر، ح. (1395). مطالعة اثر متقابل دمای سطحی آب دریا بر سرعت باد سطحی با استفاده از دادههای میدانی و ماهوارهای در خزر جنوبی (استان مازندران)، فصلنامة علمی - پژوهشی اطلاعات جغرافیایی، 25(97): 117-127.
2
ترابیآزاد، م. و محمدی، ع. (1394). مطالعه دمای سطحی آب دریا (SST) و سرعت باد در سواحل استان هرمزگان براساس دادههای ماهوارهای، پژوهشهای علوم و فنون دریایی، 10 (3): 81-91.
3
رضیئی، ط. و ستوده، ف. (1396). بررسی دقت مرکز اروپایی پیشبینیهای میانمدت جوی (ECMWF) در پیشبینی بارش مناطق گوناگون اقلیمی ایران، مجلة فیزیک زمین و فضا، 34(1): 133-147.
4
صادقینیا، ع.؛ علیجانی، ب.؛ ضیائیان، پ. و خالدی، ش. (1392). کاربرد تکنیکهای خودهمبستگی فضایی در تحلیل جزیرة حرارتی شهر تهران، مجلة تحقیقات کاربردی علوم جغرافیایی، 30: 67-90.
5
عسگری، ع. (1390). تحلیلهای آمار فضایی با ArcGIS، تهران: انتشارات سازمان فناوری اطلاعات و ارتباطات شهرداری تهران.
6
یاراحمدی، د.؛ حلیمی، م. و زارعی چقابلکی، ز. (1394). تحلیل فضایی بارش ماهانة شمال غرب ایران با استفاده از آمارة خودهمبستگی فضایی، پژوهشهای جغرافیای طبیعی، 47(3): 451-464.
7
محمدزاده، م. (1385). آشنایی با آمار فضایی، نشریة دانشجویی آمار (ندا)، 4(2): 1-12.
8
Anselin, L. (1992). Spatial data analysis with GIS: an introduction to application in the social sciences, National Center for Geographic Information and Analysis University of California, Santa Barbara, CA 93106, Technical Report, 10-92.
9
Asgari, A. (2011). Spatial Statistic Analysis with ArcGIS. Information and Communication Technology Organization of Tehran Municipality Publication, Tehran, First Edition. (In Persian)
10
Balyani, S.; Khosravi, Y.; Ghadami, F.; Naghavi, M. and Bayat, A. (2017). Modeling the spatial structure of annual temperature in Iran, Model. Earth Syst. Environ. 3: 581-593.
11
Chow, C.H. and Liu, Q. (2012). Eddy effects on sea surface temperature and sea surface wind in the continental slope region of the northern South China Sea, Geophysical Research Letters, 39: L02601.
12
Cliff, A.D. and Ord, J.K. (1981). Spatial processes: models & applications, No 44, London.
13
Getis, A. and Ord, J.K. (1992). The analysis of spatial association by use of distance statistics, Geogr Anal, 24(3):189-206.
14
Goodchild, M.F. (1986). Spatial Autocorrelation, CATMOG 47; Norwich, UK, PP. 6-25.
15
Illian, J.; Penttinen, A.; Stoyan, H. and Stoyan, D. (2008). Statistical analysis and modeling of spatial point patterns, Wiley, London.
16
IPCC (Intergovernmental Panel on Climate Change) (2013). Climate Change 2013: The physical science basis. Working Group I contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg1.
17
Katsaros, K.B. and Soloviev, A.V. (2004). Vanishing Horizontal Sea Surface Temperature Gradients at Low Wind Speeds, Boundary-Layer Meteorology, 112(2): 381-396.
18
Khosravi, Y.; Lashkari, H. and Asakereh, H. (2017). Spatial variability of water vapour in south and southwest of Iran, Quarterly Journal of MAUSAM, 68(1): 9-22.
19
Levine, N. (1996). Spatial statistics and GIS: software tools to quantify spatial patterns, J Am Plann Assoc, 62(3): 381-391.
20
Minobe, S.; Yoshida, A.K.; Komori, N.; Xie, S.P. and Small, R.J. (2008). Influence of the Gulf Stream on the troposphere, NATURE, 452: 206-210.
21
Mitchel, A. (2008). The ESRI guide to GIS analysis, volume 2: Spatial Measurements and Statistics, ESRI Press, Redlands, California.
22
Mohammadzadeh, M. (2006). Introduction to Spatial Statistics, NEDA; Student Statistical Journal, 2: 1-12. (In Persian)
23
O’Neill, L.W.; Chelton, D.B. and Esbensen, S.K. (2010). The Effects of SST-Induced Surface Wind Speed and Direction Gradients on Midlatitude Surface Vorticity and Divergence, J Clim, 23: 255-280.
24
Oerder, V.; Colas, F.; Echevin, V.; Masson, S.; Hourdin, C.; Jullien, S.; Madec, G. and Lemarié, F. (2016). Mesoscale SST–wind stress coupling in the Peru–Chile current system: Which mechanisms drive its seasonal variability?, Clim Dyn, 47(7-8): 2309-2330.
25
Oliazadeh, N. (2009). Study of Monsoon effects on a meso scale oceanic front (Ras Al Hadd), Physical Oceanography M.Sc Thesis, Islamic Azad University: Science and Research Branch. (In Persian)
26
Pionkovski, S.A. and Chiffings, T. (2014). Long-Term Changes of Temperature in the Sea of Oman and the Western Arabian Sea, International Journal of Oceans and Oceanography, 8(1): 53-72.
27
Pratchett, M.S.; Wilson, S.K.; Berumen, M.L. and McCormick, M.I. (2004). Sublethal effects of coral bleaching on an obligate coral feeding butterflyfish, Coral Reefs, 23(3): 352-356.
28
Qu, B.; Gabric, A.J.; Zhu, J.N.; Lin, D.R.; Qian, F. and Zhao, M. (2012). Correlation between sea surface temperature and wind speed in Greenland Sea and their relationships with NAO variability, Water Science and Engineering, 5(3): 304-315.
29
Raziei, T. and Sotoudeh, F. (2017). Investigation of the accuracy of the European Center for Medium Range Weather Forecast (ECMWF) in forecasting observed precipitation in different climates of Iran, Journal of the Earth and Space Physics, 43(1): 133-147. (In Persian)
30
Ren, G.Y.; Guo, G. and Xu, M.Z. (2005). Climate changes of China's mainland over the past half century, Acta Meteorol. Sin, 63(6): 942-956.
31
Reynolds, R.M. (1993). Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman—Results from the Mt Mitchell expedition, Marine Pollution Bulletin, 27: 35-59.
32
Sadeginia, A.R.; Alijani, B.; Zeaiean Firouzabadi, P. and Khaledi, S. (2013). Application of Spatial autocorrelation techniques in analyzing the heat island of Tehran. Journal of Applied research in Geographical Sciences, 30: 67-97. (In Persian)
33
Scott, L. and Getis, A. (2008). Spatial statistics. InKemp K (ed) Encyclopedia of geographic informations, Sage, Thousand Oaks, CA.
34
Song, L.C.; Gao, R. and Li, Y. (2014). Analysis of China's haze days in the winter half year and the climatic background during 1961-2012, Adv. Clim. Change Res, 5(5): 1-6.
35
Stewart, R.H. (2008). Introduction to Physical Oceanography, Texas A & M University, 57-59.
36
Sun, S.; Fang, Y.; Liu, B. and Tana, (2016). Coupling between SST and wind speed over mesoscale eddies in the South China Sea, Ocean Dynamics, 66: 1467-1474.
37
Torabi Azad, M. and Mohammadi, A. (2015). Study of Sea Surface Temperature (SST) & wind speed over coastal area of Hormozgan Province by satellite data, Journal of Marine Science & Technology, 10(3): 81-91. (In Persian)
38
Torabi Azad, M.; Aliakbari Bidokhti, A. and Salehianfar, H. (2016). Study of Induction effect on Sea Surface Temperature (SST) induced surface wind variations over the Southern Caspian Sea by satellite and in-situ observations, Scientific- Research Quarterly of Geographical Data (SEPEHR), 25(97): 117-127. (In Persian)
39
Wang, C. and Weisberg, R.H. (2001). Ocean circulation influences on sea surface temperature in the equatorial central Pacific, Journal of Geophysical Research, 106: 19,515-19,526.
40
Wang, Y.; Liu, Y.P. and Li, J.B. (2015). The effect of PM2. 5/PM10 variation based on precipitable water vapor and wind speed, J. Catastrophol, 30(1): 5-7.
41
Xie, S.P.; Deser, C.; Vecchi, G.A.; Ma, J.; Teng, H. and Wittenberg, A.T. (2010). Global warming pattern formation: Sea surface temperature and rainfall, J. Climate, 23: 966-986.
42
Yamada, I. and Thill, J.C. (2007). Local indicators of network‐constrained clusters in spatial point patterns, Geographical Analysis, 39(3): 268-292.
43
Yarahmadi, D.; Halimi, M. and Zarei Chaghabalki, Z. (2015). Analysis of Spatial Patterns of Monthly Precipitation in West and Northwest Iran Using Spatial Autocorrelation, Physical Geography Research Quarterly, 47(3): 451-464. (In Persian)
44
Zhang, G.J. and Mcphaden, M.J. (1995). Relationship between sea surface temperature and latent heat flux in the equatorial pacific, J Clim, 8: 589-605.
45
Zhang, R.H.; Li, Q. and Zhang, R.N. (2014). Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013,Sci. China Earth Sci, 57(1): 26-35.
46
ORIGINAL_ARTICLE
Effects of Spatial Movement of Arabia Subtropical High Pressure and Subtropical Jet on Synoptic and Thermodynamic Patterns of Intense Wet Years in the South and South West Iran
Introduction
Although precipitation is of great importance in all climates, it plays a vital role in the arid and semi-arid regions. The spatial and temporal distribution of precipitation in all climates is affected by special synoptic structures in which one or two systems play the controlling role. The southern Iran is located adjacent to two important climate systems whose spatial arrangements determine the timing and amount of precipitation in the mentioned region. Therefore, it is important to study the possibility of predicting the drought and wet years in this geographical region of Iran according to its strategic role in the ecology, agriculture, industry, transportation, and politics. The study was conducted on Hormozgan, Boushehr, Kohkilouyeh-va-Boyerahmad, Chahar-mahal-va-Bakhtiari, and Khuzestan Provinces in Iran.
Materials and methods
The daily precipitations in the selected stations were extracted, harmonized and arranged in a 30 year statistical period. Then, the situation of each station was determined from the viewpoint of drought and humidity using the SIPA criterion and DIP software. We have selected the years in which intense wet years were half of the selected stations, based on the mentioned criteria. These years have been selected as the samples of intense wet years. The atmospheric data of the mentioned years were extracted from the website http://www.esrl.noaa.gov, and the daily maps of these years were created at levels of 1000 and 500 hPa in the longitude of-40 degree west to 100 degree east and the latitude of zero (the equator) to 80 degree north using Grads software. The Arabian subtropical high pressure nuclei were determined for all days and their maps were created as the output maps using ArcGIS10.3 software. The data were reproduced in a matrix with the dimensions of 67×2145 based on the daily precipitation of more than 5 millimeters. The study area, located between the latitudes 0 to 80 degree north and -40 to 100 degrees east, has a number of days according to the spatial data resolution which was 2.5×2.5 geographical degree. Afterwards, justification of the data distribution according to the special values, variance percentage and accumulation variance was determined for analysis of the factors. Just 12 factors had the values larger than 1 in the primary analysis. The principle component analysis and Varimax rotation showed that concentrating on the correlation of 13 factors can explain 89.18 percent of the pattern’s behavior.
Finally, the dominant patterns in the selected intense wet year samples were extracted through studying the maps of 1000 and 500 hPa from the twelve extracted factors. Then, we have analyzed the maps of subtropical jet stream, divergent and convergent flux, special moisture, temperature blow, and etc. Moreover, the maps of different levels in all rainy days of the intense wet years were reviewed. Comparison of the repetitive patterns resulting from the review and the principle factor analysis provided similar results.
Results and discussion
Our study showed that in all the rainy days, the central nucleus of the Arabian anticyclone cell at all levels of 850 and 700 hPa was located in the east part of the longitude 45 degree E. When the anticyclone central nucleus is located in the east part of the longitude 55 degree E, the precipitations are more extensive, and cover all the regions from Khuzestan to Hormozgan. As it can be seen, in the rainy days, one or two divergent flux nuclei are located on Oman Sea or western Arab Sea and Gulf of Aden. In such condition, the streams in the lower levels of troposphere (from sea level to 850 hPa) are changed into eastern streams in northern Oman Sea and gradually on the Arab Sea. This condition is the most suitable mechanism for moisture advection towards the Sudan low pressure. In the same day, two strong nuclei of convergent moisture flux are dominated on Ethiopia and central Arabia which receive the moisture transmitted from the warm seas. The highest moisture advection towards the Sudan system is done under the layer of 850 hPa from Arab Sea, Oman Sea and the Gulf of Aden. Because of the topographic condition at levels higher than 850 hPa, this advection comes much lower, and the moisture transmitted from the transition branch may add to this moisture from the tropical convergence region. With the moisture advection in the lower levels, proper thermodynamic condition is provided for the development of convection clouds on the region. These clouds initially appear as mass clouds on Sudan and Red Sea, and then on Arabia, and then gradually move to Iran through the southern streams before trough at levels of 850 hPa and higher. Then, the clouds grow and extend through involving in the upward streams dominated on the front trough and under the subtropical jet stream, which are located on Red Sea and northwest Arabia. The proper temperature blow and diabatic warming resulting from condensation process provide a severe condition in the northern part of the jet.
Conclusion
In order to have an intense wet year in the south and south west of Iran, the eastern movement of Arabian high pressure is considered as an important factor. With the eastern movement of this high pressure, a proper synoptic condition for advection of moisture toward the precipitation system is provided. A proper condition is also provided for the extension of the Mediterranean trough towards the southern latitudes on the south east part of African desert and cold advection on the region at the middle and upper layers of troposphere. The Arabian high pressure has very high ability for wet condition, especially at the lower layers, because of its dynamic structure. Therefore, the moisture moved through the divergent flux toward the southern systems is considerable and provides significant potential energy for the convection systems. The cold advection from the northern latitudes and the warm advection from the subtropical latitudes provide proper heat gradient for intensification of the subtropical streams in the northwest domain of the Arabian high pressure. These jet streams are formed in the limits of northeast Arabia and provide proper dynamic condition for intensification of intense convection streams in the Northern Arabia and Southern Iran. The convection clouds cause intense precipitations on the region because of the access it has to the moisture of the southern warm seas and also the moisture moved from the northern branch of the tropical convergence region.
https://jphgr.ut.ac.ir/article_69790_e9340f23e12a17b0df0d512abc148803.pdf
2018-09-23
491
509
10.22059/jphgr.2018.249422.1007165
Arabian high pressure
synoptic pattern
Sudan Low
wet year
south and south west Iran
Zainab
Mohammadi
mohamadi.1040@yahoo.com
1
PhD Candidate in Climatology, Faculty of Earth Science, Shahid Beheshti University, Iran
AUTHOR
Hassan
Lashkari
dr_lashkari61@yahoo.com
2
Associate Professor of Climatology, Faculty of Earth Science, Shahid Beheshti University, Iran
LEAD_AUTHOR
احمدی گیوی، ف.؛ ایراننژاد، پ. و محمدنژاد، ع. (1389). اثر پُرفشارهای جنبحاره و سیبری بر خشکسالیهای غرب ایران، چهاردهمین کنفرانس ژئوفیزیک ایران، تهران، ۲۱ـ۲۳: 5-9.
1
حجازیزاده، ز. (1372). بررسی سینوپتیکی پُرفشار جنبحارهای در تغییر فصل ایران، پایاننامة دکترا، دانشگاه تربیت مدرس.
2
خوشاخلاق، ف.؛ عزیزی، ق. و رحیمی، م. (1391). الگوهای همدید خشکسالی و ترسالی زمستانه در جنوبغرب ایران، نشریة تحقیقاتکاربردیعلومجغرافیایی، 12(25): 57ـ77.
3
سلیقه، م. و صادقینیا، م. (1389). بررسی تغییرات مکانی پُرفشار جنبحاره در بارشهای تابستانة نیمة جنوبی ایران، فصلنامة جغرافیا و توسعه، 17: 83ـ98.
4
قائمی، ه.؛ زرین، آ.؛ آزادی، م. و فرجزاده اصل، م. (1388). تحلیل الگوی پُرفشار جنبحاره بر روی آسیا و افریقا، فصلنامة مدرس علوم انسانی، 1: 219 ـ245.
5
قویدل رحیمی. ی. (1389). نگاشت و تفسیر سینوپتیک اقلیم با استفاده از نرمافزارGrads ، سها دانش.
6
کریمی احمدآباد، م. (1386). تحلیل منابع رطوبتی بارشهای ایران، دانشکدة علوم انسانی، دانشگاه تربیت مدرس تهران.
7
کریمی احمدآباد، م. و فرجزاده، م. (1390). شار رطوبت و الگوهای فضایی- زمانی منابع تأمین رطوبت بارشهای ایران، نشریة تحقیقات کاربردی علوم جغرافیایی، 22: 109ـ127.
8
لشکری، ح. (1375). الگوی سینوپتیکی بارشهای شدید جنوبغربی ایران، پایاننامة دکتری، دانشگاه تربیت مدرس.
9
لشکری، ح. و محمدی، ز. (1394). اثر موقعیت استقرار پُرفشار جنبحارهای عربستان بر سامانههای بارشی در جنوب و جنوبغرب ایران، پژوهشهای جغرافیای طبیعی، 1: 73ـ90.
10
لشکری، ح.؛ متکان، ع.ا.؛ آزادی، م. و محمدی، ز. (1396). تحلیل همدیدی نقش پُرفشار جنبحارهای عربستان و رودباد جنبحارهای در خشکسالیهای شدید جنوب و جنوبغرب ایران، پژوهشهای دانش زمین، 8(30): 141ـ163.
11
محمدی، ز. (1396). تحلیل همدیدی نقش موقعیت مکانی پُرفشار جنبحارهای و رودباد جنبحارهای در خشکسالیها، ترسالیها، شروع و پایان و طول دورة بارشی جنوب و جنوبغرب ایران، رسالة دکتری، دانشگاه شهید بهشتی.
12
منصورفر، ک. (1388). روشهایپیشرفتةآماریهمراهبابرنامههایکامپیوتری، تهران: مؤسسة انتشارات دانشگاه تهران.
13
نجارسلیقه، م. (1385). مکانیزمهای بارش در جنوبشرق کشور، مجلة پژوهشهایجغرافیایی، 55: 1-13.
14
Ahmadi-Givi, F.; Iran-Nejad, P. and Mohammad-Nejad, A. (2010). Impact of subtropical high pressure and droughts, West Siberia, Iran, fourtten of the Geophysics Conference of Iran, Tehran, 21-23: 5-9.
15
Bell, G.D. and Bosart, L.F. (1989). A 15-year climatology of northern hemisphere 500 mb closed cyclone and anticyclone centers, Monthly Weather Review, 117, 2142-2163.
16
Barry, R.G., R.J. Chorley. (1976) Atmosphere, Weather, and Climate, Volume 208 of University Paperbacks, 460pp.
17
He, C. & Zhou, T. Clim Dyn (2014) The two interannual variability modes of the Western North Pacific Subtropical High simulated by 28 CMIP5–AMIP models, Clim Dyn, 43: 2455-2469DOI 10.1007/s00382-014-2068-x.
18
Davis, R.E.; Hayden, B.P.; Gay, D.A.; Phillips, W.L. and Jones, G.V. (1997). The North Atlantic Subtropical Anticyclone, Journal of Climate, 10: 244-278.
19
Farajzadeh Asl, M.; Ghaemi, H.; Zarrin, A. and Azadi, M. (2009). The Analysis of Spatial Pattern of Subtropical Anticyclones over Asia and Africa, MJSP, 13(1) :219-245 .( In Persian)
20
Galarneau, T.J.L.F. Bosart, and A.R. Aiyyer,(2006) Closed Anticyclones of the Subtropics and Midlatitudes: A 54-Yr Climatology (1950–2003) and Three Case Studies. Meteorological Monographs, 55, 349–392, https://doi.org/10.1175/0065-9401-33.55.349.Gao, Y. (1981). Some aspects of recent research on the Qinghai-Xizang (Tibetan) Plateau Meteorology, Bulletin of the American Meteorological Society, 62(1).
21
Ghaemi, E.; Zarrin, A.; Azadi, M. and Farajzadeh Asl, M. (2009). Analyzing the patterns of subtropical high pressure over Asia and Africa, Journal of Humanities, 1: 219-245.
22
Ghavidel Rahimi, Y. (2010). The synoptic mapping and interpreting the climate using Grads software, SahaDanesh.
23
Hastenrath F. (1990). Climate dynamic of the tropics, Climate and circulation of Th Tropics.
24
Heim Jr, R. R. (2002). A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society, 83(8), 1149-1165Hejazizadeh, Z. (1996). Evaluation of a subtropical high-pressure synoptic weather in Iran, Ph.D thesis, Tehran: Tarbiat Modares University.
25
Karimi, M. and Farajzade, M. (2012). Flow of Moisture and Spatial Patterns - Time of Iran's Rainfall Moisture Supply Resources, Journal of Applied Geosciences Research, 11(22): 109-128.
26
Krishnamurti, T.N. (1971). Tropical east-west circulations during the northern summe, Journal of Atmospheric Science, Vol. 28.
27
Krishnamurti, T.N.; Daggupaty, S.M.; Fein, J.; Kanamitsu, M. and Lee, J.D. (1973). Tibetan High and upper tropospheric tropical circulations during northern summer, Bulletin of the American Meteorological Society, 54(12): 234-249.
28
Lamb, H.H. (1972). Climate, Present, Past and Future: Fundamentals and climate now, Vol. I.
29
Lashkari, H.; Matkan, A.A.; Azadi, M. and Mohammadi, Z. (2017). Synoptic analysis of the role of Saudi Arabia subtropical high pressure subtropical and polar jet streams and severe droughts in South and South West of Iran, Journal of Researches in Earth Sciences, 8(2): 141-163. (In Persian)
30
Lashkari, H. (1996). The synoptic pattern of intense precipitations in the south west of Iran. PhD Thesis, Faculty of Human Sciences, Tarbiat Modarres University, Tehran, Iran. (In Persian)
31
Lashkari, H. and Mohammadi, Z. (2015). The effect of the location of the Arabian subtropical high pressure on the precipitation systems in the south and southwest of Iran, Researches of Natural Geography, 47(1): 73-90.
32
Lashkari, H.; Matkan, A.A.; Azadi, M. and Mohammadi, Z. (2017). Synoptic analysis of the role of Saudi Arabia subtropical high pressure subtropical and polar jet streams and severe droughts in South and South West of Iran, Journal of Researches in Earth Sciences, 8(2): 141-163.
33
MansourFar, K. (2009). The advanced statistical methods and computer programs, Tehran University press.
34
Martius, O.; Sodemann, H.; Joos, H.; Pfahl, S.; Winschall, A.; Croci-Maspoli, M.; Graf, M.; Madonna, E.; Mueller, B.; Schemm, S.; Sedláček, J.; Sprenger, M. and Wernli, H. (2013). The role of upper-level dynamics and surface processes for the Pakistan flood of July 2010, Q.J.R. Meteorol. Soc., 139: 1797-1780. doi:10.1002/qj.2082.
35
Mason R.B. and Anderson, C.E. (1963). The development and decay of the 100 mb summertime anticyclone over southern Asia, Monthly Weather Review, 93.
36
Mohammadi, Z. (2017). The synoptic analysis of the role of the spatial location of subtropical high pressure and subtropical jet on wet years, drought, start, end and duration of precipitation in the south and southwest of Iran, PhD Thesis, dr Hassan lashkari Shahid Beheshti University, Tehran, Iran. (In Persian)
37
Najarsliga, M. (2006). Rainfall Mechanisms in the South East of Iran, Geographical Research, 38(55): 1-3.
38
Neyama, Y. (1968). The morphology of the subtropical anticyclone, Journal of Meteorological Society of Japan, Vol. 46.
39
Parker, S.S.; Hawes, J.T.; Colucci, S.J. and Hayden, B.P. (1989). Limatology of 500 mb Cyclones and Anticyclones, 1950-85, Monthly Weather Review, 117pp.
40
Qian, Y.; Zhang, Q. Yao, Y. and Zhang, X. ( 2002). Seasonal variation and heat Preference of the south asia high, Advances in Atmospheric Sciences, Vol. 19.
41
Reed, T.R. (1939). Thermal aspect of the high-level anticyclone, Monthly WeatheReview, 67(7).
42
Saligheh, M. and Sadeghinia, M. (2008). Spatial variation of summer precipitation in the southern half of the subtropical high pressure Iran, Journal of Geography and Development, 17: 83-98.
43
Thompson, R.D. (1998). Atmospheric processes and systems, Routledge, 194 pp USA, 478 pp.
44
Tomozeiu, R.; Stefan, S. and Busuioc, A. (2005). Winter Precipitation Variability and Larg-scale Circulation Patterns in Romania, Journal of Theoritical and Applied Climatology, 81: 193-201.
45
Zhang, Q. and Wu, G. (2002). The bimodality of 100hPa south asia high and its relationship to the climate anomaly over east asia in summer, Journal of the Meteorological Society of Japan, 80(4).
46
ORIGINAL_ARTICLE
Future Impacts of Climate Change on Actual Evapotranspiration and Soil Water in the Talar Watershed in Mazandran Province
Introduction
Climate change is recognized as a major environmental problem by a majority of the international scientific community. According to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (IPCC, 2007), “global atmospheric concentrations of carbon dioxide, methane and nitrous oxide have increased markedly as a result of human activities since 1750 and now it exceeded pre-industrial values”. This report further suggests that most of the observed increase in global average temperatures since the mid-twentieth century is very likely resulted from rising anthropogenic greenhouse gas concentrations and that, if not controlled, climate effects such as rising sea level, disruption to weather patterns, and ocean acidification pose serious harms to human health, water supplies, agricultural systems, economic performance, and global security. Such projections have triggered calls for prompt and coordinated action to reduce greenhouse gas emissions and adapt to changes in the climate system. Paying attention to climate change event affecting all sections of hydrologic cycle, the goal of this research is to study these phenomena on actual evapotranspiration and soil humidity that play important role in this cycle, with available water for plant, decrease or increase in annual runoff. This can affect all sections of the environmental factors.
Materials and methods
Watershed models are essential for studying hydrologic processes and their responses to both natural and anthropogenic factors, but due to model limitations in representation of complex natural processes and conditions, these models usually must be calibrated prior to application to closely matching with SWAT (Soil and Water Assessment Tool). This is a comprehensive and semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. Several calibration techniques have been developed for SWAT, including manual calibration procedures and automated procedures using the shuffled complex evolution method and other common methods. In SWAT, a basin is delineated into sub-basins, which are then further subdivided into Hydrologic Response Units (HRUs). HRUs consist of homogeneous land use and soil type (also, management characteristics) and based on two options in SWAT, they may either represent different parts of the sub-basin area or a dominant land use or soil type (also, management characteristics). With this semi-distributed (sub-basins) set-up, SWAT is attractive for its computational efficiency as it offers some compromise between the constraints imposed by other model types such as lumped, conceptual or fully distributed, and physically based models. For this goals in the boundary of Talar watersheds of Mazandaran province, we selected 8 stations for precipitation, 5 for temperature and discharge, with Shirgah-Talar station in output. After preparing maps and necessary weather data, we conducted output of SWAT model for this watershed. In this research, we used water year of 2003-2004 until 2006-2007 by a duration of 4 years for calibration and water year of 2008-2009 by duration of 2 years for validation. For calibration and validation of this model, we also used SWAT-CUP package software and SUFI2 program.
Results and discussion
The entire Talar watershed is divided into 219 Hydrologic Response Unite in 23 sub-watersheds. For assessment of SWAT model from climate change on actual evapotranspiration and available soil water, the SWAT model, further run for this area with new condition. In this stage with definition of HRU for the model, only variated precipitation and temperature are entered into the model to study the influence of these effects on assessment factors in output of the model. To do so, we used variable precipitation and temperature, forecasted by LARS-WG model as one of the important model output for random data of weather condition. After change of daily temperature and precipitation for these stations, variation values enter into SWAT model for second run.
Conclusion
It can be concluded that the mean daily evapotranspiration (to year) in the time of calibration and validation, is increased for all duration and higher evaporation in majority of future month. This can be compared with present time that is high index evaporation in May, June, July and August. Study about available water shows unregularly process in decrease or increase of this factor and at least available water in May, June, July and August in the future time that affected hydrologic regularity of the watershed. This can provide water for plant in any month where water is necessary as another environmental factor of area.
https://jphgr.ut.ac.ir/article_69791_b53f90a7531abe3850fe5401ced3ff4a.pdf
2018-09-23
511
529
10.22059/jphgr.2018.202185.1006831
hydrologic cycle
temperature and precipitation
calibration
validation
modeling
Abbas
Gholami
mehrdad_53200@yahoo.com
1
Assistant Professor of Environmental Sciences and Engineering, Shomal University, Iran
AUTHOR
Mahmood
Habibnejad Roshan
roshanbah@yahoo.com
2
Professor of Agriculture and natural Resources, Sari University, Iran
LEAD_AUTHOR
Kaka
Shahedi
kaka.shahedi@gmail.com
3
Assistant Professor of Agriculture and Natural Resources, Sari University, Iran
AUTHOR
Mehdi
Vafakhah
vafakhah@modares.ac.ir
4
Associate Professor, Natural Recourses, Tarbiat Modarres University, Iran
AUTHOR
Karim
Solaymani
k.solaimani@sanru.ac.ir
5
Professor of Agriculture and Natural Resources, Sari University, Iran
AUTHOR
آبابایی، ب. و سهرابی، ت. (1388). ارزیابی عملکرد مدل SWATدر حوضة آبریز زایندهرود، مجلة پژوهشهای حفاظت آب و خاک، 16(3): 41ـ58.
1
اکبری مجدر، ح.؛ بهرهمند، ع.؛ نجفینژاد، ع. و احدبردی، ش. (1392). شبیهسازی جریان روزانة رودخانة چهلچای استان گلستان با مدل SWAT، نشریةپژوهشهای حفاظت آ ب و خاک، 20(3): 253ـ259.
2
ذهبیون، ب.؛ گودرزی، م. و مساح بوانی، ع. (1389) کاربرد مدل SWAT در تخمین رواناب حوضه در دورههای آتی تحت تأثیر تغییر اقلیم، نشریۀ پژوهشهای اقلیمشناسی، 3: 43ـ58.
3
سادات آشفته، پ. و مساح بوانی، ع. (1389). تأثیر تغییر اقلیم بر دبیهای حداکثر: مطالعة موردی، حوزۀ آیدوغموش، آذربایجان شرقی، مجلة علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، 14(53): 25ـ39.
4
سلمانی، ح.؛ رستمی خلج، م.؛ محسنی ساروی، م.؛ روحانی، ح. و سلاجقه، ع. (1391). بهینهسازی پارامترهای مؤثر بر بارش- رواناب در مدل نیمهتوزیعی SWAT (مطالعة موردی: حوضة آبخیز قزاقلی استان گلستان)، فصلنامة علمی- پژوهشی اکوسیستمهای طبیعی ایران، 3(2): 85ـ100.
5
عارفی اصل، ا.؛ نجفینژاد، ع.؛ کیانی، ف. و سلمان ماهینی، ع. (1392). تعیین مناطق بحرانی تولید رسوب در آبخیز چهلچای استان گلستان با استفاده از مدل SWAT، نشریةپژوهشهای حفاظت آب و خاک، 20(5): 193ـ205.
6
علیزاده، ا.؛ ایزدی، ع.؛ داوری، ک.؛ ضیایی، ع.ن.؛ اخوان، س. و حمیدی، ز. (1392). برآورد تبخیر- تعرق واقعی در مقیاس سال- حوضه با استفاده از SWAT، نشریةآبیاری و زهکشی ایران، 7(2): 243ـ258.
7
گزارش طرح تلفیق آبخیزداری حوضة تالار (1380). دفتر مطالعات و ارزیابی آبخیزها، معاونت آبخیزداری وزارت جهاد کشاورزی.
8
Ababei, B. and Sohrabi, T. (2009). Assessing the performance of SWAT model in Zayandeh Rud Watershed, J. of Water and Soil Conservation, 16(3): 41-58.
9
Abbaspour, K.C.; Rouholahnejad,B.; Vaghefi ,S.; Srinivasan R.; Yang H.; Kløve B. (2015). A continental-scale hydrology and water quality model for Europe:Calibration and uncertainty of a high-resolution large-scale SWAT model, Journal of Hydrology, 524: 733-752.
10
Abbaspour,K.; Faramarzi, M.; Ghasemi, S. and Yang, H. (2009). Assessing the impactof climate change on water resources in Iran, Water Resour. Res., 45(10).
11
Abbaspour, K.; Schulin, R.; Schlappi, E. and Fluhler, H. (1996). A Bayesian approach for incorporating uncertainty and data worth in environmental projects, Environ. Model. Assess, 1: 151-158.
12
Akbari Mejdar, H.; Bahremand, A.R.; Najafinejad, A. and Sheikh, V.B. (2013). Assessing the performance of SWAT model in Zayandeh Rud watershed, J. of Water and Soil Conservation ,20(3): 253-259.
13
Alizadeh, A.; Izady, A.; Davary, K.; Ziaei, A.N.; Akhavan, S. and Hamidi, Z. (2013). Estimation of Actual Evapotranspiration at Regional – Annual scale using SWAT, Iranian Journal of lrrigation and Drainage, 7(2): 243-258.
14
Andrade, M.A.; Mello, C.R. and Beskow, S. (2013). Hydrological simulation in a watershed with predominance of Oxisol in the Upper Grande river region, MG-Brazil, Rev. Bras. Eng. Agric. Ambient, 17: 69-76 (in Portuguese).
15
Aragão, R.; Cruz, M.A.S.; Amorim, J.R.A.; Mendonça, L.C.; Figueiredo, E.E. and Srinivasan, V.S. (2013). Sensitivity analysis of the parameters of the SWAT model and simulation of the hydrosedimentological processes in a watershed in the northeastern region of Brazil, Rev. Bras. Ciênc. Solo, 37: 1091-1102 (in Portuguese).
16
Arefi Asl, A.; Najafinejad, A.; Kiani, F. and Salmanmahiny, A. (2013). Identification of critical sediment production regions yield in Chehelchai watershed using SWAT model, J. of Water and Soil Conservation, 20(5): 193-205.
17
Arnold, J.G.; Srinivasan, R.; Muttiah, R.S. and Williams, J.R. (1998). Large area hydrologic modeling and assessment, Part I: Model development, J. Am. Water Resour. Assoc, 34(1): 73-89.
18
Bailey, Ian; Revell, Piers (2015) . Climate Change, International Encyclopedia of the Social & Behavioral Sciences, 2nd edition, Vol. 3, School of Geography, Earth and Environmental Sciences, Plymouth University, Plymouth, UK
19
Baker, T.J. ; Miller, S.N. (2013). Using the Soil and Water Assessment Tool (SWAT) to assess land use impact on water resources in an East African watershed, J. Hydrol, 486:100-111.
20
Bastiaanssen, W.G.M.; Pelgrum, H.; Wang, J.; Ma, Y.; Moreno, J.F.; Roerink, G.J. and Van der Val, T. (1998). A remote sensing surface energy balance algorithm for land (SEBAL): 2. Validation, Journal of Hydrology, 213-229.
21
Brown, L.C; T.O. Barnwell, Jr. (1987). The enhanced water quality models QUAL2E and QUAL2E-UNCAS documentation and user manual.EPA document EPA/600/3-87/007.USEPA. Athens.GA.
22
Brzozowski, J.; Miatkowski, Z.; Śliwiński, D.; Smarzyńska, K. and Śmietanka, M. (2011). Application of SWATmodel to small agricultural catchment in Poland, J. Water Land Dev, 15: 157-166.
23
Chen, Ji. and Yiping, Wua (2012). Advancing representation of hydrologic processes in the Soil and WaterAssessment Tool (SWAT) through integration of the Topographic Model (Topmodel) features, Journal of Hydrology, 420-421: 319-328.
24
Christensen, N.S.; Wood, A.W.; Voisin, N.; Lettenmaier, D.P. and Palmer, R.N. (2004). The effects of climate change on the hydrology and water resources of the Colorado River Basin, Climatic Change, 62(1): 337-363.
25
Davidson, E.A. and Janssens, I.A. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change, Nature, 440(7081): 165-173.
26
Demirel, C.; Mehmet, A; Anabela Venancio, B and Ercan Kahya, C. (2009). Flow forecast by SWAT model and ANN in Pracana basin, Portugal, Advances in Engineering Software, 40: 467-473.
27
Diabat, M.; Haggerty, R. and Wondzell, S.M. (2013). Diurnal timing of warmer air under climate change affects magnitude, timing and duration of stream temperature change, Hydrol. Process, 27(16): 2367-2378.
28
Dobler, C.; Bürger, G. and Stötter, J. (2012). Assessment of climate change impacts on flood hazard potential in the Alpine Lech watershed, J. Hydrol, 460-461: 29-39.
29
Dubrovsky, M. (1996). Validation of the stochastic Weather Generator Met&ROLL, Meteorogickeo Zpravy, 49: 12q-1380.
30
Durães, F.; Mello, C.R. and Naghettini, M. (2011). Applicability of theSWATmodel for hydrologic simulation in Paraopeba river basin, MG. Cerne, 17: 481-488.
31
Eum, H. and Simonovic, S.P. (2012). Assessment on variability of extreme climate events for the Upper Thames River basin in Canada, Hydrol. Process, 26(4): 485-499.
32
Fukunaga D. C. ; Roberto A. C. ;Sidney S. Z, ;Laís T, O,; Marco A, C, C.(2015) Application of the SWAT hydrologic model to a tropical watershed at Brazil ,Catena 125: 206–213.
33
Wolock, D. and McCabe, G. (1999), Estimates of Runoff Using Water-Balance and Atmospheric
34
General Circulation Models. Journal of the American Water Resources Association, 35(6):1341-1350.
35
Hawkins, A; Enrique R.; Vivoni a,; Agustin R,; Giuseppe M, ; Erick ,R; Francina D. (2015) A climate change projection for summer hydrologic conditions in a Gretchen semiarid watershed of central Arizona, Journal of Arid Environments 118 , 9-20
36
Intergovernmental Panel on Climate Change (IPCC) (2012). Summary for policymakers. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, A Special Report of Working Groups I and II of theIntergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 1-19.
37
Kite, G.W. and Droogers, P. (2000). Comparing evapotranspiration estimates from satellites hydrological models and field data, Journal of Hydrology, 229: 3-18.
38
Li , Lu,; Ismaïla D,;Chong ,X,;Frode S.(2015) Hydrological projections under climate change in the near future by RegCM4in Southern Africa using a large-scale hydrological model, Journal of Hydrology S0022,1649(15)00378-9
39
Milly, P.C.D. (1994). Climate, soil water storage, and the average annual water balance, Water Resources Research, 30: 2143- 2156.
40
Musaua, J.; Sanga, J.; Gathenyaa, J. and Luedeling, E. (2015). Hydrological responses to climate change inMt, Elgon watersheds, Journal of Hydrology: Regional Studies, Contents lists available at ScienceDirect, Journal of Hydrology: Regional Studies (In Press).
41
Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; King, K.W. and Williams, J.R. (2005). Soil and Water Assessment Tool (SWAT) Theoretical Documentation, Blackland ResearchCenter, Texas Agricultural Experiment Station, Temple, Texas (BRC Report02-05).
42
Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Srinivasan, R. and Williams, J.R. (2000). Soil and water assessment tool user’s manual – version 2000, Soil and Water Research Laboratory, Agricultural Research Service, Grassland, 808 East Blackland Road, Temple, Texas.
43
Qiao, L.; Chris B.; Zou, R.; Elaine S.(2015) Calibration of SWAT model for woody plant encroachment using paired experimental watershed data, Journal of Hydrology 523 : 231–239
44
Sadat Ashofteh, P. and Massah Bovani, A. (2009). Uncertainty of Climate Change Impact on the Flood Regim, Case Study: Aidoghmoush Basin, East Azerbaijan, Iran, Iran-Water Resources Researc, 14(53): 25-39.
45
Safeeq, M. and Fares, A. (2012). Hydrologic response of a Hawaiian watershed to future climate change scenarios, Hydrol. Process, 26(18): 2745-2764.
46
Salmani, H.; Rostami Khalaj, M.; Mohseni Saravi, M.; Rohani, H. and Salajeghe, A. (2012). Optimmization of afecte parameter on runoff-precipitatuion in SWAT model(case study in ghazaghely watershed of golestan province), Quarterly Natural Ecosystems of Iran, 3(2): 85-100.
47
Semenov, M.A. and Brooks, R.J. (1999). Spatial interpolation of the LARSWG stochastic weather generator in great Britain, Climate Research, 11: 137-148.
48
Singh V.; Niteenkumar B.; Sagar S.; Apurba K.; Sharma,j.(2013) Hydrological stream flow modelling on Tungabhadra catchment: parameterization and uncertainty analysis using SWAT CUP, CURRENT SCIENCE, VOL. 104, NO. 9: 1187-1199.
49
Talar watershed combining project report (2001). Watershed assessment and study registry, Watershed assistant of Jihade-Agricultural Ministry of Iran.
50
Task Group on Data and Scenario Support for Impact and Climate Assessment (TGICA) Intergovernmental Panel on Climate Change.June (2007). General Guidelines On The Use Of Scenario Enario Data For Climate IMPACT And Adaptation Assessment Version 2, Prepared by T.R.
51
Thampi, S.G.; Raneesh, K.Y. and Surya, T.V. (2010). Influence of scale on SWAT model calibration for streamflow in a river basin in the humid tropics, Water Resour. Manag., 24: 4567-4578.
52
Tian, Y.; Xu, Y.P. and Zhang, X. (2013). Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models, Water Resour. Manage, 27 (8): 2871-2888. in Spain: Water resources, agriculture and land, Journal of Hydrology, 518: 243-249.
53
Wang, D.; Hejazi, M.; Cai, X. and Valocchi, A.J. (2011). Climate change impact on meteorological, agricultural, and hydrological drought in central Illinois, Water Resour. Res., 47(9).
54
Wellen C .;George B.;Tanya, L.;Duncan,B.;(2014) Quantifying the uncertainty of nonpoint source attribution in distributed water quality models: A Bayesian assessment of SWAT’s sediment export Predictions ,Journal of Hydrology 519 : 3353–3368
55
William, J.R. and Hann, R.W. (1972). HYMO, a problem oriented computer language for building computer models, Water Resour.Res., 8(1): 79-85.
56
Wolock, D. and McCabe, G. (1999). Estimates of Runoff Using Water-Balance and AtmosphericXu, Y.P.; Zhang, X. and Tian, Y. (2012). Impact of climate change on 24-h design rainfall depth estimation in Qiantang River Basin, East China, Hydrol. Process., 26(26): 4067-4077.
57
Xu, Y.P.; Zhang, X.; Ran, Q. and Tia, Y. (2013). Impact of climate change on hydrology of upper reaches of Qiantang River Basin, East China, Journal of Hydrology, 483: 51-60.
58
Yang, J.; Reichert, P.; Abbaspour, K.C. and Yang, H. (2007). Hydrological modelling of theChaohe Basin in China: Statistical model formulation and Bayesian inference, Journal of Hydrology, 340: 167-182.
59
Yeh, William. W.-G. (1986). Review of parameter identification procedures in ground water hydrology: The inverse problem, Water Resour. Res, 22: 95-108.
60
Yu, Pao-Shan; Tao-Chang, Y. and Chih-Kang, W. (2002). Mpact of climate change on water resources in southern Taiwan, J. Hydrol, 260: 161-175.
61
Zahbion, B.; Goodarzi, M. and Massah Bouani, A.R. (2010). Using of SWAT model for assume of runoff in the future duration effecting of climate change, Journal of climate research, 3(4): 43-58.
62
Zhang ,X.;Yue, X.; Guangtao, F. (2014) Uncertainties in SWAT extreme flow simulation under climate change. Journal of Hydrology. 515, 205–222.
63
ORIGINAL_ARTICLE
Economic Effects of Land Use and Land Cover Changes through Remote Sensing Techniques and Survey Studies, Behbahan
Introduction
Human beings have always attempted to meet their requirements by using agriculture lands for many years. This is changed in such a way that today with present population with diversity of human needs and overuse of land, they created many adverse effects. Wide areas of natural resources disregarding ecological principles to meet their needs have been turned into the degraded lands, while many of these lands have not been cultivated with a high erosion potential. Having knowledge of land use and its changes and reviewing possible causes are important in planning and policy-making in the country. The ratio of land use changes can help to anticipate upcoming changes and perform appropriate actions. Remote sensing techniques can be helpful to detect changes in land-use patterns as great resources for management and planning. The aim of this study was to investigate changes in land use and land cover based on the data derived from satellite images, since these changes along with environmental effects have different impacts on the economy and living conditions of beneficiaries. In addition, the assessment of the following direct economic impacts on land use change considered living and livelihoods of all residents of the area.
Materials and methods
Behbehan city is located in the southeastern areas of Khuzestan and limited to the cities of Kohgiluyeh and Boyer Ahmad provinces. The job activity of the majority of the villagers is agriculture and animal husbandry. There are three types of vegetation, bushes and grass cover in Behbehan. In this study, we used Landsat satellite images, Landsat TM data in 1991 and the Landsat OLI satellite images of in 2016, general aerial photographs of 1: 20000, numerical topographic map of 1: 25000, and GPS data (Etrex Model). In the first step, the satellite images were processed and geometric and atmospheric corrections are made to prepare the raw image data. Then, we have used maximum likelihood method for supervised classification. After selecting training samples of the images from 1991 to 2016, we used maximum likelihood classification in ENVI to classify the images. Using the clasification method, we divided the images into 6 classes of woods, grassland, agricultural areas, abandoned land, residential areas and water groups. The classification accuracy for image was assessed by using the kappa index. The communities affected by changes in land use were identified in order to evaluate the effects of changes on economic dimension of beneficiaries. At the end, we have analyzed the changes in economic situation of people due to landuse changes.
Results and discussion
After classifying maximum likelihood classification in ENVI, land use maps were obtained from 1991 and 2016. The maps are related to the two time periods (1991-2016) evaluated by overlaying the two maps in GIS environment to obtain change map. The results indicate that the forest area in 1991 was 9348.93 hectares and with 4.57 percent decrease reached to 8812.53 hectares in 2016. The area of pasture is also decreased by 36.63 percent and it has decreased from 86596.92 hectares in 1991 to 82297.71 hectares in 2016. The agriculture land-use has increased 11.72% from191.61 hectares in 1991 to 1567.26 hectares in 2016. The area of abandoned lands has also increased from 15137.19 hectares in 1991 to 18413.91 hectares until now. Residential areas have also increased from 1044.27 hectares in 1991 to 2337.39 hectares in 2016. Water level is faced with a reduction in 2016 and it was more than 1399.05 hectares in 1991 and reached to 444.69 hectares in 2016, a decrease of 8.13 percent. The reduction of 536.4 acre in the use of forest and palm groves caused £ 25064958204decrease. A decrease in 4299.21 acre of pasture caused £2923462800 of devaluation. Increase in the agricultural use of lands caused increased income, equivalent to £ 825390000000. Increase in abandoned lands caused decrease in income and consequently devaluation of £ 318168655183.
Conclusion
Given that in this study period, residential areas has increased due to increased urban population and immigration, the need of people for housing has, consequently, increased. The pasture area has been reduced in this period since they are changed these lands over time in recent years. Because of increase in the use of agricultural lands and unprincipled irrigation using traditional methods, water resource and droughts are, consequently, declined in recent years. With increasing population and development of residential areas, it is needed to fix the problem and this reduces natural resources (pasture, water and forest). Although land use change and decrease in the level of natural resources during this period had not so negative complications, but continuation of this process and non-normative change of land use can have more negative consequences in the long run. Therefore, we have to determine uses which are compatible with the environmental potential and capacity of lands.
https://jphgr.ut.ac.ir/article_69792_3e1d26135c28e408bb611337715714f9.pdf
2018-09-23
531
543
10.22059/jphgr.2018.231707.1007040
Land Use
land use changes
Remote Sensing
economy of residents
Behbahan
Fatemeh
Shojaei
fshojaeimordad@yahoo.com
1
MSc in Range Management, Behbahan Khatam Alanbia University of Technology, Iran
AUTHOR
Somayeh
Dehdari
s_dehdari2000@yahoo.com
2
Assistant Professor of Natural Resources, Behbahan Khatam Alanbia University of Technology, Iran
LEAD_AUTHOR
Zohreh
Khorsandi Koohanestani
khorsandi_zohre@yahoo.com
3
Assistant Professor of Natural Resources, University of Agricultural Sciences and Natural Resources Khuzestan, Iran
AUTHOR
آذینمهر، م.؛ بهرهمند، ع.ر. و کبیر، آ. (1394). شبیهسازی اثر سناریوهای تغییر کاربری اراضی روی هیدروگراف جریان حوضة آبخیز دینور با استفاده از مدل هیدرولوژیکی توزیعی- مکانی WetSpa، نشریة علمی- پژوهشی مهندسی و مدیریت آبخیز، 7(4): ۵۰۰ـ510.
1
آسوده، م. (1393). مطالعة اثرات اقتصادی تغییرات کاربری و پوشش اراضی با استفاده از روشهای سنجش از دور و مطالعات پیمایشی در جنوب اصفهان، پایاننامة کارشناسی ارشد.
2
ابراهیمی، پ.؛ اصلاح، م. و سلیمی کوچی، ج. (1395). بررسی کارایی مدل زنجیرهای مارکوف در برآورد تغییرات کاربری اراضی و پوشش زمین با استفاده از تصاویر ماهوارهای Landsat، نشریة علمی- پژوهشی علوم و مهندسی آبخیزداری ایران، 10(34): ۸۵ـ۹۳.
3
جوزی، س.ع.؛ رضایان، س. و بندیان، س.س. (1393). بررسی اثرات اقتصادی- اجتماعی تخریب جنگلهای دو هزار و سه هزار تنکابن، نشریة علوم و مهندسی محیط زیست، 1(3): ۲۷ـ40.
4
درویشصفت، ع.ا. (1377). برآورد صحت نقشههای موضوعی پایگاه داده GIS. پنجمین همایش سامانههای اطلاعات جغرافیایی، تهران، ایران.
5
رضوانی، م.ر. (1390). توسعة گردشگری روستایی با رویکرد گردشگری پایدار، چ۲، انتشارات دانشگاه تهران.
6
روستا، ز.؛ منوری، س.م.؛ درویشی، م. و فلاحتی، ف. (1391). کاربرد دادههای سنجش از دور RS و سیستم اطلاعات جغرافیایی GIS در استخراج نقشههای کاربری اراضی شهر شیراز، مجلة آمایش سرزمین، 4(6): 149- 164.
7
سنجری، ص. و برومند، ن. (1392). پایش تغییرات کاربری/ پوشش اراضی در سه دهة گذشته با استفاده از تکنیک سنجش از دور (مطالعة موردی: منطقة زرند کرمان)، مجلة کاربرد سنجش از دور و GIS در علوم منابع طبیعی، 1: 66-57.
8
علیزاد گوهری، ن.؛ لطیفی، م.؛ نصری، م.؛ یگانه، ح. و سرسنگی، ع.ر. (1391). تشخیص تغییرات استفاده از زمین در شهر نائین با استفاده از دادة ماهوارهای لندست، مجلة علمی- پژوهشی خاورمیانه، 11(4): ۴۳۹ـ444.
9
فیضیزاده، ب. و حاج میر رحیمی، م.و. (1387). آشکارسازی تغییرات کاربری اراضی شهرک اندیشه با استفاده از روش طبقهبندی شیگرا، همایش ژئوماتیک، سازمان نقشهبرداری کشور: ۱ـ10.
10
قربانی، س.؛ زرع کار، ا.؛ کاظمی، ب. و یاوری، ا.م. (1392). برآورد خسارت عملکرد حفاظت از منابع آبی در جنگل با استفاده از سنجش از دور (مطالعة موردی: جنگلهای چالوس)، مجلة کاربرد سنجش از دور و GIS در علوم منابع طبیعی، 1: ۲۷ـ۳۷.
11
قربانی، م.؛ مهرابی، ع.ا.؛ ثروتی، م.ر. و نظری سامانی، ع.ا. (1389). بررسی تغییرات جمعیتی و اثرگذاریهای آن بر تغییرات کاربری اراضی (مطالعة موردی: منطقة بالای طالقان)، نشریة مرتع و آبخیزداری، مجلة منابع طبیعی ایران، 63(1): ۷۵ـ 88.
12
محمداسماعیل، ز. (1389). پایش تغییرات کاربری اراضی کرج با استفاده از تکنیک سنجش از دور، مجلة پژوهشهای خاک (علوم خاک و آب)، 24(1): ۸۱- ۸۸.
13
محمدیاری، ف.؛ توکلی، م. و اقدر، ح. (1395). ارزیابی و پهنهبندی کیفیت آب زیرزمینی مناطق مهران و دهلران از لحاظ کشاورزی با روشهای زمینآمار، مجله علمی- پژوهشی علوم و مهندسی آبیاری، 39(4): 71- 83.
14
مشیری، س.ر. و قماشپسند، م.ت. (1391). تحلیلی پیرامون اثرات و پیامدهای تغییر کاربری اراضی کشاورزی در روستاهای بخش مرکزی شهرستان لاهیجان در دهة اخیر، نشریة چشمانداز جغرافیایی (مطالعات انسانی)، 7(21): ۱ـ 13.
15
مطیعی لنگرودی، س.ح.؛ رضوانی، م.ر. و کاتب ازگمی، ز. (1391). بررسی اثرات اقتصادی تغییر کاربری اراضی کشاورزی در نواحی روستایی (مطالعة موردی: دهستان لیچارکی حسنرود بندرانزلی)، مجلة پژوهش و برنامهریزی روستایی، 1: ۱ـ23.
16
مهدوی، م.؛ قدیری معصوم، م. و محمدی یگانه، ب. (1382). نقش منابع طبیعی (مطالعة موردی: طالقان)، پایاننامة کارشناسی ارشد دانشگاه تهران.
17
میرعلیزاده فرد، س.ر. و علیبخشی، س.م. (1395). پایش و پیشبینی روند تغییرات کاربری اراضی با استفاده از مدل زنجیرة مارکوف و مدلساز تغییر کاربری اراضی (مطالعة موردی: دشت برتش دهلران، ایلام)، نشریة سنجش از دور و سامانة اطلاعات جغرافیایی در منابع طبیعی، 7(2): ۳۳ـ45.
18
هاشمیان، م. (1383). مطالعة روشهای ارزیابی دقت برای طبقهبندی دادههای سنجیدهشده از راه دور، پایاننامة کارشناسی ارشد، دانشگاه خواجه نصیرالدین طوسی.
19
Alizad Gohari, N.; Latifi, M.; Nasri, M.; Yeganeh, H. and Sarsangi, A.R. (2012). Change Detection of Land Use Changes in Naein City of Using Satellite Data of Landsat, Middle-East, Journal of Scientific Research, 11(4):439-444.
20
Asoodeh, M. (2014). Master's thesis, A study of the economic impact of land use changes and land cover using remote sensing methods and survey studies in the south of Isfahan.
21
Azin Mehr, M.; Bahrehmand, A.A.R. and Kabir, A. (2015). Simulated the effects of land use change scenarios on the hydrograph of Daynor watershed using located- distributed hydrological model, WetSpa, scientific-research, Journal of engineering and watershed management, 7(4): 500- 510.
22
Ebrahimi, P.; Eslah, M. and Salimi Kochi, J. (2016). The stusy of Markov chain model to estimate the efficiency of land use and land cover changes using Landsa satellite images, science and Engineering, Journal of Watershed Management in Iran, 30(34): 85-93.
23
Fayzizadeh, B. and Haj Mir Rahimi, M.V. (2008). Land use change detection of Andishah town using object-oriented classification in Geomatics congress, National mapping agency, p. 1-10.
24
Ghorbani, M.; Mehrabi, A.A.; Sarvati, M.R. and Nazari Samani, A. (2010). Evaluation of demographic change and transition effects on land use changes (Case study: Bala Taleghan area ), Journal of pasture and watershed, Journal of Natural Resources in Iran, 63(1): 75- 88.
25
Gorbani, S.; Zarkar, A.; Kazemi, B. and Yavari, A.M. (2013). Assessing damages on water resource conservation in the forest using remote sensing (Case Study :Chalous Forests), Journal of remote sensing and GIS applications in natural resource sciences, 1: 27-37.
26
Hashemian, M. (2004). Study of accuracy assessment techniques for classification of remotely sensed data MSc Thesis, K, N, Toosi University of Technology.
27
Irwin, E.G. and Geoghegan, J. (2001). Theory, data, methods: developing emigration and land -use change at the watershed level: A GIS-based approach in Central Mexico, Agricultural Systems, 90: 62-78.
28
Jozi, S.A.; Rezaian, S. and Bandian, S.S. (2014). The study of socio-economic effects in destruction of two and three thousand jungles, Environmental Science and Engineering journal, 1(3): 27-40.
29
Kenneth, M. and Gunter, M. (2012). Monitoring Land-Use Change in Nakuru Kenya Using Multi-Sensor Satellite Data, Advance remote sensing, p.74-78.
30
Lambin, E.F. and Meyfroidt, P. (2010). Land use transitions: Socio-ecological feedback versus socio-economic change, Land Use Policy, 27: 108-118.
31
Lopez, E.; Bocco, G.; Menduza, M.; Valezquez, A. and Aguirre Rivera, J.R. (2006). Peasant emigration and land-use change at the watershed level: A GIS-based approach in Central Mexico, Agricultural systems, 90: 62- 78.
32
Mahdavi, M.; GhadiriMasoum, M. and Mohammadi Yeganeh, B. (2003). The role of natural Resources (Case study: Taleghan). MSc thesis, University of Tehran, Iran.
33
Mather, A.S.; Fairbairn, J. and Needle, C.L. (1999). The course and drivers of the forest transition: the case of France, Journal of Rural Studies, 15(1): 65-90.
34
Mir Alizadehfard, S.R. and Alibakhshi, I.S.M. (2016). Monitoring and forecasting of land use change by applying Markov chain model and land change modeler (Case study: Dehloran Bartash plains, Ilam), RS & GIS for Natural Resources, 7(2): 33-45.
35
Mohammad Ismail, Z. (2010). Land use change detection in Karaj using remote sensing techniques, Journal of preceding studies of soil (soil and water sciences), 24(1): 81-88.
36
Moshiri, S.R. and Qomashpasand, M.T. (2012). An analysis of the effects and consequences of changes in land use for agriculture in the central rural city of Lahijan in last decade, geographical vision magazine (human studies), 7(21): 1- 13.
37
Motiee Langroodi, S.H.; Rezvani, M.R. and Kateb Azgami, Z. (2012). The study of economic effects on agricultural land use changes in rural areas (Case Study: Hassan rood district of Bandar Anzali ), Research and rural planning magazine, 1: 1-23.
38
Rezvani, M.R. (2011). The development of rural tourism with sustainable tourism approach, Tehran, Tehran University Press, Second edition.
39
Roosta, Z.; Monaveri, S.M.; Darvishi, M. and Falahati, F. (2012). Application of remote sensing data , RS and geographic information system,GIS, in land use maps of the Shiraz, The land use Journal, 4(6): 149 -164.
40
Sanjari, S. and Boroumand, N. (2013). Monitoring changes in land use / covering the lands in the last three decades using remote sensing techniques (Case study: Zarand region of Kerman), Journal of Remote Sensing and GIS resources in natural sciences, 1: 57-66.
41
Tahir, F. Madad, A. Muhammad, Sh. and Inayat, Kh. (2013). Response of Community towards Gender Dysphorics, Greener Journal of Social Sciences, 3(1): 55- 66.
42
Wu, Qiong; Li, Hong-qing; Wang, Ru-song; Paulussen, Juergen; He, Yong; Wang, Min; Wang, Bihui and Wang, Zhen (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS, Landscape and urban planning, Article in press.
43
ORIGINAL_ARTICLE
Detection of Dust Storms in Jazmoriyan Drainage Basin Using Multispectral Techniques and MODIS Image
Introduction
Based on the importance of dust storm phenomenon, negative effects of the dusts on human health and social and economic consequences, it is essential to identify the dust source locations for planning and to eliminate the production factors of the dust storms. The last improvement in remote sensing makes a situation for using the satellite image for exploration of the dust sources. In this study, the MODIS image data were used for detection of the sources of dust storm in Jazmoriyan seasonal wetland and their corresponding watersheds. In order to achieve this target, we used three methods including Xie (2009), Zhao et al. (2010) and Liu (2011). The performance of the methods was investigated by AOD and horizontal visibility. In order to simulate the path of dust aerosol, we used HYSPLIT model Lagrangian approach for forward trajectory.
Materials and methods
Jazmurian is a dried wetland in a closed drainage basin in south-east Iran. Population growth, irrigation in surrounding farmland, dam building on feeding river, climate change and drought made the wetland to dry. The Jazmoriyan wetland, 300 km2 in area, is located between Sistan-va-Baluchistan and Kerman provinces, 58° 39' to 59° 14' E and 27° 10' to 27° 38' N.
In this research, we have used field data (horizontal visibility), satellite data (MODIS level1B and Level 2 products), and meteorological data and Hysplit model output data.
The Xie (2009) method is based on decision tree through several indexes. Zhao et al. (2010) method was developed for dust detection on earth and ocean in daytime. Liu and Liu (2011) suggested the Thermal Infrared Integrated Dust Index (TIIDI) for separating the dust, sand surface and cloud. Representing the intensity of dust storm is the main advantage of this method. The most important feature of this method is to show the intensity of the dust storm.
Statistical analysis was conducted using Excel (Microsoft). Image processing was done with ENVI 5.3 software. Afterward the appropriate band for dust detection was identified. Then, some image was selected for extracting the thresholds.
Finally, based on extracted thresholds, dust storm over the Jazmoriyan watershed by MODIS images data on January 4, 2017, to January 7, 2017, was detected. The intensity of the dust was classified by TIIDI method, and dust source was introduced based on the region with the highest dust intensity. Three critical points of dust were identified with this method.
Results and discussion
The results have demonstrated that these methods are useful for dust detection. The results of dust detection show that, there aren't any dust storms in Jazmoriyan on January 4, 2017. The dust storm began at 6:40 on January 5, 2017, in center of swamp Jazmotiyan and it had increasing trend until 9:55 at that time. Following of this process, the dust storm reaches to the highest txtent on January 6, 2017, at 7:25 and decreasing trend was started at 9 AM at the same day. The dust storm was finished in Jazmoriyan watershed in next day (January 7, 2017). Furthermore, the two-days forward air-mass trajectories with HYSPLIT model show that the dusty air masses at all altitudes are moved to the south-east part of Iran and will affect Oman Sea and Makran Mountain. The analysis of meteorological maps showed that a jet with a speed more than 30 m/s has covered all study area. It increased the dust storm possibility in the region. Based on the results, the extracted bands and thresholds in Jazmoriyan watershed is in agreement with other researchers. The results of dust detection obtained from MODIS confirm the results from obtained myd04 products and horizontal visibility.
Conclusion
Unsuitable distribution of synoptic station and lack of ground monitoring stations around the Jazmoriyan swamp are the issues in dust monitoring. MODIS image data can be used for dust storm detection. Performance of Xie (2009), Zhao 2010, et al. and TIIDI methods were investigated. The results of these methods using MODIS image data on January 4, 2017, to January 7, 2017, showed that the dust storm that began on January 5, 2017, was approximating at 6:40 AM. The dust had an increasing trend until the next day. The dust was spreading in a vast area on January 6, 2017, at 7:25 and completely was disappeared on January 7, 2017. In addition, the results of path tracing of aerosols of dust source represent the aerosol movement to the south-east Iran, Makran Coasts, and Persian Gulf. This is same as the results of other researcher.
https://jphgr.ut.ac.ir/article_69793_ea094d173e6ac1580d557bb1152b666a.pdf
2018-09-23
545
562
10.22059/jphgr.2018.248345.1007159
dust detection
Jazmoriyan swamp
TIIDI method
AOD
Farzaneh
Qaderi Nasab
gaderi_f@alumni.ut.ac.ir
1
PhD Candidate in Water Structures, Shahid Bahonar University of Kerman, Iran
AUTHOR
Mohhamad Bagher
Rahnama
mbr@uk.ac.ir
2
Associate Professor of Water Structures, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
ادارة کل حفاظت محیط زیست استان سیستان و بلوچستان (1393). تهیة نقشة پایة منابع اکولوژیک تالاب جازموریان و معرفی آن به عنوان یکی از مناطق تحت حفاظت سازمان حفاظت محیط زیست با استفاده از RS و GIS.
1
شمشیری، س.؛ جعفری، ر.؛ سلطانی، س. و رمضانی، ن. (1393). آشکارسازی و پهنهبندی ریزگردهای استان کرمانشاه با استفاده از تصاویر ماهوارهای MODIS، بومشناسی کاربردی، 3(8).
2
عطایی، ش.؛ محمدزاده، ع. و آبکار، ع.ا. (1394 الف). شناسایی گرد و غبار با استفاده از روش درخت تصمیمگیری از تصاویر سنجندة مادیس، مجلة علمی- پژوهشی علوم و فنون نقشهبرداری، 4(4): 151-160.
3
عطایی، ش.؛ آبکار، ع.ا. و محمدزاده، ع. (1394 ب). شناسایی گرد و غبار با استفاده از شاخص TIIDI بهبودیافته و بهکارگیری دادههای سنجندۀ مادیس، محیطشناسی، 41(3): 563ـ572.
4
مصباحی، ح. (1392). مدلسازی منابع آب با استفاده از نرمافزار MODSIM با هدف نگرش یکپارچه به چالشها و راهکارهای حوضة آبریز هامون جازموریان، پنجمین کنفرانس مدیریت منابع آب.
5
Ackerman, S.A. (1989). Using the radiative temperature difference at 3.7 and 11 μm to tract dust outbreaks, Remote Sensing of Environment, 27(2): 129-133.
6
Ackerman, S.A. (1997). Remote sensing aerosols using satellite infrared observations, Journal of Geophysical Research: Atmospheres, 102(D14): 17069-17079.
7
Ataei, Sh.; Abkar, A.A. and Mohammadzadeh, A. (2015a). Dust detection using improved TIIDI and applying MODIS sensor data, Journal of Environmental Studies, 41(3): 572-563.
8
Ataei, Sh.; Mohammadzadeh, A. and Abkar, A.A. (2015b). Using Decision Tree Method for Dust Detection from MODIS Satellite Image, JGST 2015, 4(4): 151-160.
9
Csavina, J.; Field, J.; Félix, O.; Corral-Avitia, A.Y.; Sáez, A.E. and Betterton, E.A. (2014). Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates, Science of The Total Environment, 487: 82-90.
10
Draxler, R.R. and Hess, G.D. (1998). An overview of the HYSPLIT_4 modelling system for trajectories, Australian meteorological magazine, 47(4): 295-308.
11
Environmental Protection Agency of Sistan and Baluchestan provinc (2014). Preparation of the ecosystem resources map of Jazmourian wetland and its introduction as one of the protected areas of the Environmental Protection Agency using RS and GIS.
12
Fu, Q.; Thorsen, T.J.; Su, J.; Ge, J.M. and Huang, J.P. (2009). Test of Mie-based single-scattering properties of non-spherical dust aerosols in radiative flux calculations, Journal of Quantitative Spectroscopy and Radiative Transfer, 110(14): 1640-1653.
13
Guo, J.; Xia, F.; Zhang, Y.; Liu, H.; Li, J.; Lou, M. ... and Zhai, P. (2017). Impact of diurnal variability and meteorological factors on the PM 2.5-AOD relationship: Implications for PM 2.5 remote sensing, Environmental Pollution, 221: 94-104.
14
Huang, J.; Fu, Q.; Su, J.; Tang, Q.; Minnis, P.; Hu, Y. ... and Zhao, Q. (2009). Taklimakan dust aerosol radiative heating derived from CALIPSO observations using the Fu-Liou radiation model with CERES constraints, Atmospheric Chemistry and Physics, 9(12): 4011-4021.
15
Huang, J.; Wang, T.; Wang, W.; Li, Z. and Yan, H. (2014). Climate effects of dust aerosols over East Asian arid and semiarid regions, Journal of Geophysical Research: Atmospheres, 119(19).
16
Kaufman, Y.J. and Tanré, D. (1998). Algorithm for remote sensing of tropospheric aerosol from MODIS. NASA MODIS Algorithm Theoretical Basis Document, Goddard Space Flight Center, 85: 3-68.
17
Kaufman, Y.J.; Tanré, D.; Gordon, H.R.; Nakajima, T.; Lenoble, J.; Frouin, R. ... and Teillet, P.M. (1997). Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect, Journal of Geophysical Research: Atmospheres, 102(D14): 16815-16830.
18
Li, X.; Ge, L.; Dong, Y. and Chang, H.C. (2010). Estimating the greatest dust storm in eastern Australia with MODIS satellite images. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International (pp. 1039-1042). IEEE.
19
Liu, Y. and Liu, R. (2011). A thermal index from MODIS data for dust detection. In Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International (pp. 3783-3786). IEEE.
20
Mesbahi, H. (2013). A unified approach Water Resources Modeling to Challenges and Solutions in the Watershed Basin Hamoun Jazmourian Using MODSIM with the Purpose of Unified Approach, The Fifth Conference on Water Resources Management, Tehran.
21
Miller, S.D. (2003). A consolidated technique for enhancing desert dust storms with MODIS, Geophysical Research Letters, 30(20).
22
Moulin, C.; Lambert, C.E.; Dayan, U.; Masson, V.; Ramonet, M.; Bousquet, P. ... and Bergametti, G. (1998). Satellite climatology of African dust transport in the Mediterranean atmosphere, Journal of Geophysical Research: Atmospheres, 103(D11): 13137-13144.
23
Pineda-Martinez, L.F.; Carbajal, N.; Campos-Ramos, A.A.; Noyola-Medrano, C. and Aragón-Piña, A. (2011). Numerical research of extreme wind-induced dust transport in a semi-arid human-impacted region of Mexico, Atmospheric ejenvironment, 45(27): 4652-4660.
24
Qu, J.J.; Hao, X.; Kafatos, M. and Wang, L. (2006). Asian dust storm monitoring combining Terra and Aqua MODIS SRB measurements, IEEE Geoscience and Remote Sensing Letters, 3(4): 484-486.
25
Rashki, A.; Arjmand, M. and Kaskaoutis, D.G. (2017). Assessment of dust activity and dust-plume pathways over Jazmurian Basin, southeast Iran, Aeolian Research, 24: 145-160.
26
Reidmiller, D.R.; Hobbs, P.V. and Kahn, R. (2006). Aerosol optical properties and particle size distributions on the east coast of the United States derived from airborne in situ and remote sensing measurements, Journal of the atmospheric sciences, 63(3): 785-814.
27
Riehl, H. (1961). Jet streams of the atmosphere, US Government Printing Office.
28
Rolph, G.; Stein, A. and Stunder, B. (2017). Real-time environmental applications and display system: Ready, Environmental Modelling & Software, 95: 210-228.
29
Shamshiri, S.; Jafari, R.; Soltani, S. and Ramazani, N. (2014). Dust detection and mapping in Kermanshah province using satellite imagery of Modis, Iranian Journal of Applied Ecology, 3(8).
30
Shao, Y. and Dong, C.H. (2006). A review on East Asian dust storm climate, modelling and monitoring, Global and Planetary Change, 52(1): 1-22.
31
Stunder, B.J. (1997). NCEP model output-FNL archive data: TD-6141. Prepared for National Climatic Data Center (NCDC). Technical report, NOAA Air Resources Laboratory, Silver Spring, MD.This document and archive grid domain maps are also available at http://www. arl. noaa. gov/ss/transport/archives. html.
32
Tian, J. and Chen, D. (2010). Spectral, spatial, and temporal sensitivity of correlating MODIS aerosol optical depth with ground-based fine particulate matter (PM2. 5) across southern Ontario, Canadian Journal of Remote Sensing, 36(2): 119-128.
33
Wang, J. and Christopher, S.A. (2003). Intercomparison between satellite‐derived aerosol optical thickness and PM2. 5 mass: implications for air quality studies, Geophysical research letters, 30(21).
34
Li, X., Ge, L., Dong, Y., & Chang, H. C. (2010, July). Estimating the greatest dust storm in eastern Australia with MODIS satellite images. In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International (pp. 1039-1042). IEEE.
35
Xie, Y. (2009). Detection of smoke and dust aerosols using multi-sensor satellite remote sensing measurements, George Mason University.
36
Zhang, L.; Cao, X.; Bao, J.; Zhou, B.; Huang, J.; Shi, J. and Bi, J. (2010). A case study of dust aerosol radiative properties over Lanzhou, China. Atmospheric Chemistry and Physics, 10(9): 4283-4293.
37
Zhao, C., Chen, S., Leung, L. R., Qian, Y., Kok, J., Zaveri, R., & Huang, J. (2013). Uncertainty in modeling dust mass balance and radiative forcing from size parameterization. Atmospheric Chemistry & Physics Discussions, 13(7).
38
Zhao, T.X.P.; Ackerman, S. and Guo, W. (2010). Dust and smoke detection for multi-channel imagers, Remote Sensing, 2(10): 2347-2368.
39
ORIGINAL_ARTICLE
Accuracy of PERSIANN-CDR Precipitation Satellite Database in Simulation Assessment of Runoff in SWAT Model on Maharlu Basin
Introduction
Rainfall is the most important meteorological factor driving the hydrology of river basins. For the development and management of water resources, it is required to have a reliable coverage of rain gauges, rainfall satellite data and weather radars. Well-maintained ground-based rainfall stations give the best rainfall estimation with high accuracy over time for a small area. The spatial sampling error becomes higher in estimating rainfall when using interpolation techniques. This issue becomes particularly critical in data scarce regions with unevenly distributed rain gauge stations. This is recognized as one of the principal sources of uncertainty in hydrological modeling. Currently, with more and more global precipitation datasets developed, a decline is seen in the application of reanalysis products in hydrological modelling. As a hot research field, many studies focus on the application of directly measured precipitation data on flood risk evaluation at basin scales and discuss their potential for hydrological prediction of ungauged/poorly gauged basins. In this study, we used PERSIANN-CDR gridded database precipitation for modeling runoff in SWAT model in Maharlu Lake basin.
Materials and methods
The PERSIANN-CDR was used as a gridded database of precipitation for modeling runoff in SWAT model in Maharlu Lake basin. The PERSIANN-CDR is initially compared with rain gauged data and after that it was used as input to SWAT model. SWAT model was calibrated by rain gauge data during 1983 to 2013. The Warmup period set to 3 years. Three discharge stations were used for calibration. Correlation coefficient of, Nash-Sutcliff, POD, CSI, FAR, RMSE, ME and BIAS had been assessed to determine the accuracy of PERSIANN-CDR. SWAT model uncertainty and sensitivity were calculated in SWAT-CUP by SUFI2 method.
Results and discussion
Comparing the PERSIANN-CDR in monthly scales, we found that this satellite wheatear database less estimates the variables in all months. The results showed average of correlation coefficient is 0.6 and RMSE showed a high error in rainy seasons. In SWAT model, calibration period was set to 1983 to 2010 with validation from 2011 to 2013. Calibration with gauged data showed satisfactory Nash-Sutcliff and R2 statistical indices about 0.6 for the area. The best result was occurred in Chenar-Sokhte-khosh discharge station, R2 was about 72% in calibration and 81% in validation. Calibration with PERSIANN-CDR database showed that this database is not good enough to be used in this semi-distributed model. In Chenar-Sokhte-khosh discharge station, R2 is calculated about 0.59 and Nash-Sutcliff about 0.21. R-factor and P-factor was presented about 0.5 in all discharge stations. These factors show that uncertainty calculation was occurred in good form. The simulation of annual runoff showed that the average runoff simulated using observation database was 1.68 m3/s, the mean runoff simulated by PERSIANN-CDR is 0.84 m3/s, and mean runoff of discharge stations were 1.77 cubic meters per second. On monthly scale, PERSIANN-CDR estimated less runoff like rainfall over all months. Both databases simulate runoff values relative to those recorded in the autumn months less than actual values. The results of this study, which were conducted using the PERSIANN-CDR satellite product, unlike the other studies with global exploratory bases, displayed that in the simulation with the SWAT model, this base cannot be accurately high in simulation. The error of estimating precipitation has been entered directly into the model and caused an error.
Conclusion
In this study, with the accuracy of precipitation data, PERSIANN-CDR satellite data on rainfall estimation revealed that this database estimated precipitation values less than real values in all months of the year. Runoff simulation using this satellite product expresses the explanatory factor and the efficiency of Nash-Sutcliff about 0.59 and 0.21.
Despites the time series of precipitation values, this satellite database has a high correlation with the actual values observed on rain-fed stations, but as the findings show the estimated rainfall values are always lower than actual recorded values. Based on the findings from this study, the PERSIANN-CDR satellite is not very accurate on the area of the Maharlou Lake Basin, located in eastern Zagros. In the semi-distributed SWAT model, it cannot simulate runoff. Therefore, it is suggested that before applying estimated rainfall data, this satellite database will have its error and bias values compared with the observed data on rain gauge stations and, then, the estimated precipitation values are corrected based on the bias.
https://jphgr.ut.ac.ir/article_69794_871dc44e4861bdb2c5079152a73a046c.pdf
2018-09-23
563
576
10.22059/jphgr.2018.238898.1007096
hydrology
Maharlu Lake
PERSIANN-CDR
Runoff
SWAT Model
Mohammad Reza
Eini
mohammad.eini@ut.ac.ir
1
MSc in Water Resources Engineering, Aburaihan Campus, University of Tehran, Iran
AUTHOR
Saman
Javadi
javadis@ut.ac.ir
2
Assistant Professor of Water Engineering, Aburaihan Campus, University of Tehran, Iran
LEAD_AUTHOR
Majid
Delavar
m.delavar@modares.ac.ir
3
Assistant Professor of Water Resources Engineering, Tarbiat Modares University, Iran
AUTHOR
Mohammad
Darand
m.darand@uok.ac.ir
4
Associate Professor, Department of Meteorology, Natural Resources Faculty, Kurdistan University, Iran
AUTHOR
حاجیحسینی، ح.؛ حاجیحسینی، م.؛ نجفی، ع؛ مرید، س. و دلاور، م. (1393). ارزیابی تغییرات متغیرهای هواشناسی در بالادست حوضة هیرمند طی سدة گذشته با استفاده از دادههای اقلیمی CRU و مدل SWAT، مجلة تحقیقات منابع آب، ۱۰(3): 38-52.
1
دارند، م. و زند کریمی، س. (1395). ارزیابی دقت دادههای بارش مرکز اقلیمشناسی بارش جهانی بر روی ایران، مجلة ژئوفیزیک ایران، 10(۳): 95-113.
2
عینی، م.ر.؛ جوادی، س. و دلاور، م. (1397). ارزیابی عملکرد دادههای بازتحلیلشدة پایگاههای اقلیمی جهانی CRU و NCEP CFSR در شبیهسازی هیدرولوژیکی مدلSWAT ، مطالعة موردی: حوضة آبریز مهارلو، مجلة تحقیقات منابع آب ایران، 14(۱):32-44.
3
عینی، م.ر.؛ جوادی، س.؛ دلاور، م. و دارند، م. (1397). ارزیابی دادههای بارش پایگاه ملی اسفزاری در برآورد رواناب و پایش خشکسالی منطقهای، مجلة اکوهیدرولوژی، 5(۱): 99-110.
4
غضنفری مقدم، م.؛ علیزاده، ا.؛ موسوی باسگی، م.؛ فرید حسینی، ع. و بنایان اول، م. (1390). مقایسة مدل PERSIANN با روشهای درونیابی به منظور کاربرد در تخمین مقادیر بارندگی روزانه، نشریة آب و خاک، 1: ۲۰۷ـ215.
5
کتیرایی بروجردی، پ.س. (1392). مقایسة دادههای بارش ماهانة ماهوارهای و زمینی در شبکهای با تفکیک زیاد روی ایران، مجلة ژئوفیزیک ایران، 4: ۱۴۹ـ 160.
6
مسعودیان، ا.؛ کیخسروی کیانی، م.ص. و رعیتپیشه، ف. (1393). معرفی و مقایسة پایگاه دادة اسفزاری با پایگاههای دادة GPCC ،GPCP ، و CMAP، تحقیقات جغرافیایی، 29(۱): 73ـ 87.
7
Auerbach, D.A.; Easton, Z.M.; Walter, M.T.; Flecker, A.S. and Fuka, D.R (2016). Evaluating weather observations and the climate forecast system reanalysis as inputs for hydrologic modelling in the tropics, Hydrol. Process, 30: 3466-3477.
8
Casse, C.; Gosset, M.; Peugeot, C.; Pedinotti, V.; Boone, A.; Tanimoun, B.A. and Decharme, B. (2015). Potential of satellite rainfall products to predict Niger Riverflood events in Niamey, Atmos. Res., 163: 162-176.
9
Darand, M.; Amanollahi, J. and Zandkarimi, S. (2017). Evaluation of the performance of TRMM Multi-satellite Precipitation, Analysis (TMPA) estimation over Iran, Atmospheric Research, 190: 121-127.
10
Darand, M.; Zerafati, O.; Kefayatmotlagh, R. and Samandar, R. (2015). Comparison between global and regional precipitation data bases with base station Asfazari precipitation Iran, Geographical Research, 3: 30-47.
11
Darand, M. and Zand Karimi, S. (2016). Evaluation of the accuracy of the Global Precipitation Climatology Center (GPCC) data over Iran, Journal of Iran Geophysical, 103: 95-113.
12
Dile, Y.T. and Srinivasan, R. (2014). Evaluation of CFSR climate data for hydrologic prediction in data-scarce watersheds: An application in the Blue Nile River Basin, J. Am. Water Resour. Assoc., 50: 1226-1241.
13
Eini, M.R., Javadi, S. and Delavar, M. (2018). Evaluating the performance of CRU and NCEP CFSR global reanalysis climate datasets, in hydrological simulation by SWAT model, Case Study: Maharlu basin, Iran-Water Resources Research 14(1), 32-44 DOI: 10.13140/RG.2.2.24445.41444.
14
Eini, M.R., Javadi, S., Delavar, M., and Darand, M. (2018). Assessment of Asfezari national database precipitation data in runoff evaluating and monitoring regional drought, Iranian Journal of EcoHydrology, (5)1, 95-110, http://dx.doi.org/10.22059/ije.2017.235625.643.
15
Fuka, D.R.; Walter, M.T.; MacAlister, C.; Degaetano, A.T.; Steenhuis, T.S. and Easton, Z.M. (2014). Using the climate forecast system reanalysis as weather input data for watershed models, Hydrol. Process, 28: 5613-5623.
16
Ghazanfari, M.M.; Alizadeh, A.; Mosavi, B.M.; Farid, H.A. and Banaian, A.M. (2010). Comparing PERSIANN with interpolation methods in order to application estimation daily rainfall, Soil and Water Journal, 1: 207-215.
17
HajiHosseini, H.; HajiHosseini, M.R.; Morid, S. and Delavar, M. (2013). Assessment of changes in hydro-meteorological variables upstream of Helmand Basin during the last century using CRU data and SWAT model, Iran-Water Resources Research, 17: 38-52.
18
Katiraie-Boroujerdy, S.P. (2012). Comparing satellite and ground base monthly data rainfall in high spatial over Iran, Iran Geophysics Journal, 1: 149:160.
19
Katiraie-Boroujerdy, S.P.; Nasrollahi, N.; Hsu, KL. and Sorooshian, S. (2016). Quantifyin the reliability of four global datasets for drought monitoring over a semiarid region: Theor, Appl. Climatol., 123: 387-398.
20
Masoudian, A.; Keykhosravi, M. and Rayat Pisheh, F. (2015). Intruduction and evaluation Asafzari database with GPCC, GPCP, CMAP, Geographical Research, 19: 73-88.
21
Mei, Y.W.; Anagnostou, E.N.; Nikolopoulos, E.I. and Borga, M. (2014). Error analysis of satellite precipitation products in mountainous basins, J. Hydrometeorol, 15: 1778-1793.
22
Miao, C.; Ashouri, H.; Hsu, K-L.; Sorooshian, S. and Duan, Q. (2015). Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China, Journal of Hydrometeorology, 16: 1387-1396.
23
Monteiro, J.A.F.; Strauch, M.; Srinivasan, R.; Abbaspour, K. and Gücker, B. (2016). Accuracy of grid precipitation data for Brazil: Application in river discharge modelling of the Tocantins catchment, Hydrol. Process., 30: 1419-1430.
24
Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Srinivasan, R. and Williams, J.R. (2011). Soil and Water Assessment Tool, User Manual, Version 2012, Grassland, Soil and Water Research Laboratory, Temple, Tex, USA.
25
Nikolopoulos, E.I.; Anagnostou, E.N. and Borga, M. (2013). Using high-resolution satellite rain-fall products to simulate a major flash flood event in northern Italy, J. Hydrometeorol., 14: 171-185.
26
Nkiaka, E.; Nawaz, N. and Lovett, JC. (2017). Evaluating global reanalysis datasets as input for hydrological modelling in the sudano-sahel region, Hydrology, 4(1): 13.
27
Qian Z., Weidong Xuan; Li, Liu and Yue-Ping, Xu (2016). Evaluation and hydrological application of precipitation estimates derived from PERSIANN-CDR, TRMM 3B42V7, and NCEP-CFSR over humid regions in China, Hydrol. Process, 42: 1832-1861.
28
Thiemig, V.; Rojas, R.; Zambranobigiarini, M. and Roo, A.D. (2013). Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo basin, J. Hydrol, 499: 324-338.
29
ORIGINAL_ARTICLE
Relationship between Arctic Oscillation and Precipitation in Iran
Introduction
Simultaneous variations in weather and climate over widely separated regions have long been noted in the meteorological literature. Such variations are commonly referred to as "teleconnections". In the extra-tropics, teleconnections link neighboring regions mainly through the transient behavior of atmospheric planetary-scale waves. One of the most well-known teleconnections is the Arctic Oscillation. Arctic Oscillation is the leading mode of extratropical circulation from the surface to the lower-level stratosphere in the northern hemisphere. Fluctuations in the Arctic Oscillation create a seesaw pattern in which atmospheric pressure at polar and middle latitudes fluctuates between negative and positive phases. For instance, a positive Arctic Oscillation phase is accompanied by low pressure over the north Polar Regions and high pressure at the mid-latitudes. These features are reversed in a negative Arctic Oscillation phase. The Arctic Oscillation trends are highly correlated with atmospheric phenomena such as variability in sea level pressure, storm tracks and precipitation throughout northern hemisphere.
Materials and methods
The purpose of this study is to examine the impact of the Arctic Oscillation on the frequency of days with rainfall event in Iran. For doing so, we used three dataset. 1) The gridded daily precipitation of GPCC in a 1˚ latitude × 1˚ longitude resolution. These data have been extracted for 154 grid points within the political boundary of Iran. Therefore, our initial matrix of daily precipitation have been consisted of 9125 rows, one for each day from March 21, 1988 (Farvardin 1, 1367), to March 20, 2013 (Esfand 29, 1391), and 154 columns, one for each grid point in Iran. 2) The daily Arctic Oscillation index from the Climate Prediction Center of the National weather service, NOAA. These data have formed a matrix in 9125×1. 3) The mean daily geopotential height data of 700 hPa level at 2.5˚ × 2.5˚ grid resolution from National Center Environmental/ Department of Energy (NCEP-DOE). This matrix is also consisted of 9125 rows, one for each day from March 21, 1988, to March 20, 2013, and 5328 columns, one for each grid point in northern hemisphere. In this study, was used to investigate the impact of the Arctic Oscillation on the frequency of rainfall evens. Then, the lag correlation was used to find the highest correlation between the Arctic Oscillation and the frequency of the days with rainfall event. Based on this, the frequency of rainfall event was investigated. Finally, the long term mean geopotential height of the 700 hPa level in association with the highest correlation was analyzed. MATLAB software was employed to analyze the data.
Results and discussion
The statistic and its significant test showed that the relationship between the Arctic Oscillation and the frequency of days with rainfall event is significant from October 23- November 21 (Aban) to April 21 – May 21 (Ordibehesht). Then, the obtained results of lag correlation showed simultaneous correlation in the two months of October 23- December 21 (Aban and Azar) and lag time for December 22 – March 20 (winter) and March 21- May 21 (Farvardin and Ordibehesht). Based on the obtained correlation results, the frequency of the days with rainfall event from November to May was investigated during the positive and the negative phases of the Arctic Oscillation. The results have indicated that the probability of rainfall events during the positive phase of the Arctic Oscillation is the highest. A survey on mean daily geopotential height of 700 hPa level, when the Arctic oscillation is positive, reveals that 700-hPa level is anomalously low over the polar caps and over the region of the Icelandic Low while it is anomalously high over the western half of Africa to southwest Europe. This pattern leads to enhanced pressure gradient over the eastern half of Atlantic and northwest Europe. This 700 hPa level pattern forms a trough over the eastern Mediterranean. Positive vorticity and northerly flow in this area create dynamic conditions to develop low pressure system. When the system is accompanied with other weather conditions can cause rainfall in Iran. In addition to the eastern Mediterranean trough, the sub-tropical high pressure also plays an important role in the rainfall events. Reduction in the zonal range of high pressure at the time of the occurrence of a positive phase of the Arctic Oscillation and its retreat from the southern half of Iran and even in the formation a divergent core over north Arab Sea as the most important source of humidity can increase the probability of rainfall event in Iran.
Conclusion
The results showed that the impact of the Arctic Oscillation on the frequency of the days with rainfall event starts from October 23- November 21 (Aban) and continues to April 21 – May 21 (Ordibehsht). The probability of rainfall event during the positive phase of the Arctic Oscillation is the highest as well. Synoptic pattern of 700 hPa showed that the positive phase of the Arctic Oscillation increase pressure gradient over the eastern half of Atlantic. This pattern provides conditions to develop eastern Mediterranean trough in mid troposphere and low pressure system in low troposphere over the eastern Mediterranean. Decrease of pressure due to the positive phase of Arctic Oscillation in mid-latitude affects subtropical high pressure and retreat from southern half of Iran. Its retreat and even formation of a divergent core over north Arab Sea can increase the probability of rainfall events in Iran.
https://jphgr.ut.ac.ir/article_69795_6ebfd295dadfb897b20f0da1fe5a954c.pdf
2018-09-23
577
591
10.22059/jphgr.2018.244741.1007135
rainfall event
Arctic Oscillation
lag correlation
frequency
Iran
Zahra
Hojati
hojati.zahra_h@yahoo.com
1
PhD Candidate in Climatology, University of Isfahan, Iran
AUTHOR
Seyed Abolfazl
Masoodian
procista@yahoo.ie
2
Professor of Climatology, University of Isfahan, Iran
LEAD_AUTHOR
احمدی، م. (1392). تحلیل ارتباط بین الگوهای پیوند از دور و ویژگیهای بارش ایران، رسالة دکتری اقلیمشناسی، دانشگاه تربیت مدرس.
1
امیدوار، ک. و جعفری ندوشن، مو (1393). اثر نوسان قطبی بر نوسانهای دما و بارش فصل زمستان در ایران مرکزی، فصلنامة جغرافیایی سرزمین، 41: 65ـ76.
2
حجازیزاده، ز. و فتاحی، ا. (1386). تحلیل الگوهای سینوپتیکی بارشهای زمستانة ایران، مجلة جغرافیا، 3: 89ـ107.
3
خسروی، م. (1383). بررسی روابط بین الگوهای چرخش جوی کلانمقیاس نیمکرة شمالی با خشکسالی سالانة سیستان و بلوچستان، مجلة جغرافیا و توسعه، 167ـ188.
4
عساکره، ح. (1390). مبانی اقلیمشناسی آماری، زنجان: انتشارات دانشگاه زنجان.
5
مسعودیان، س.ا؛ کیخسروی کیانی.م.ص. و رعیتپیشه، ف. (1393). معرفی و مقایسة پایگاه داده اسفزاری با پایگاههای دادة GPCC، GPCP، و CMAP، مجلة تحقیقات جغرافیایی، ۲۹(1): 73ـ88.
6
میری، م.؛ عزیزی، ق.؛ خوشاخلاق، ف. و رحیمی، م. (1395). ارزیابی آماری دادههای شبکهای بارش و دما با دادههای مشاهدهای در ایران، نشریة علمی- پژوهشی علوم و مهندسی آبخیزداری ایران، 35: 39ـ51.
7
یاراحمدی، د. و عزیزی، ق. (1386). تحلیل چندمتغیرة ارتباط میزان بارش فصلی ایران و نمایههای اقلیمی، پژوهشهای جغرافیایی، 62: 161ـ 174.
8
Ahmadi, M. (2013). Analysis of the relationship between teleconnection patterns and rainfall characteristics of Iran, Phd dissertational climatology, Tarbiat Modares University.
9
Asakereh, H. (2011). The Basics of Statistical Climatology, Zanjan: Zanjan University Press, First Edition.
10
Chen, Y.; Guo, S.; Liu, Y.; Ju, J. and Ren, J. (2017). Interannual Variation of the Onset of Yunnan’s Rainy Season and Its Relationships with the Arctic Oscillation of the Preceding Winter, Atmospheric and Climate Sciences, 7(2): 210-222.
11
Givati, A. and Rosenfeld, D. (2013). The Arctic Oscillation, climate change and the effects on precipitation in Israel, Atmospheric research, 132: 114-124.
12
Glantz, M.H., Katz, R.W. and Nicholls, N. eds., 1991. Teleconnections linking worldwide climate anomalies (Vol. 535). Cambridge: Cambridge University Press.
13
Gong, D.Y.; Gao, Y.; Guo, D.; Mao, R.; Yang, J.; Hu, M. and Gao, M. (2014). Interannual linkage between Arctic/North Atlantic Oscillation and tropical Indian Ocean precipitation during boreal winter, Climate dynamics, 42(3-4): 1007-1027.
14
Gong, D. and Wang, S. (2003). Influence of Arctic Oscillation on winter climate over China, Journal of Geographical Sciences, 13(2): 208-216.
15
Hejazizadeh, Z. and Fatahi, A. (2007). Analysis of synoptic patterns of rainfall in Iran, Quarterly Geography, 3: 89-107.
16
Hu, Q. and Feng, S. (2010). Influence of the Arctic oscillation on central United States summer rainfall, Journal of Geophysical Research: Atmospheres, 115(D1).
17
Jovanović, G.; Reljin, I. and Reljin, B. (2008). The influence of Arctic and North Atlantic Oscillation on precipitation regime in Serbia, In IOP Conference Series: Earth and Environmental Science (4(1): 012025). IOP Publishing.
18
Khosravi , M. (2004). A Survey On The Relations OF The Northern Hemisphere Large Scale Circulation Patterns With Sistan & Baluchestan Annual Droughts, Geography and Development Journal, 2(3): 167-188.
19
Kutzbach, J.E. (1970). Large-scale features of monthly mean Northern Hemisphere anomaly maps of sea-level pressure, Monthly Weather Review, 98(9): 708-716.
20
Lorenz, E.N. (1951). Seasonal and irregular variations of the Northern Hemisphere sea-level pressure profile, Journal of Meteorology, 8(1): 52-59.
21
Mao, R.; Gong, D.Y.; Yang, J. and Bao, J.D. (2011). Linkage between the Arctic Oscillation and winter extreme precipitation over central-southern China, Climate Research, 50(2-3): 187-201.
22
Masoodian, S.A.; Keikhosravi Kiany, M.S. and Rayatpishe, F. (2014). Introducing and comparing the Asfezari database with GPCC, GPCP and CMAP databases, Geographical Research journal, 29(1): 73-88.
23
McCabe‐Glynn, S.; Johnson, K.R.; Strong, C.; Zou, Y.; Yu, J.Y.; Sellars, S. and Welker, J.M. (2016). Isotopic signature of extreme precipitation events in the western US and associated phases of Arctic and tropical climate modes, Journal of Geophysical Research: Atmospheres, 121(15): 8913-8924.
24
Miri, M.; Azizi, G.; Khoshakhlagh, M. and Rahimi, M. (2017). Evaluation Statistically of Temperature and Precipitation Datasets with Observed Data in Iran, Iranian Journal of Watershed Management Science and Engineering ,10(35): 39-51.
25
Omidvar, K. and Jafari Nadoshan, M. (2014). Study of Arctic Oscillation Effect on Temperature and Precipitation Fluctuations at winter in Central Iran, Quarterly Geographical journal of territory (Sarzamin), 41: 65-76.
26
Pavlović Berdon, N. (2012). The impact of Arctic and North Atlantic Oscillation on temperature and precipitation anomalies in Serbia, Geographica Pannonica, 16(2): 44-55.
27
Raziei, T.; Bordi, I. and Pereira, L.S. (2011). An application of GPCC and NCEP/NCAR datasets for drought variability analysis in Iran, Water resources management, 25(4): 1075-1086.
28
Thompson, D.W. and Wallace, J.M. (1998). The Arctic Oscillation signature in the wintertime geopotential height and temperature fields, Geophysical research letters, 25(9): 1297-1300.
29
Wallace, J.M. and Gutzler, D.S. (1981). Teleconnections in the geopotential height field during the Northern Hemisphere winter, Monthly Weather Review, 109(4): 784-812.
30
Wen, M.; Yang, S.; Kumar, A. and Zhang, P. (2009). An analysis of the large-scale climate anomalies associated with the snowstorms affecting China in January 2008, Monthly weather review, 137(3): 1111-1131.
31
Wu, B. and Wang, J. (2002). Possible impacts of winter Arctic Oscillation on Siberian high, the East Asian winter monsoon and sea-ice extent, Advances in Atmospheric Sciences, 19(2): 297-318.
32
Yang, H. (2011). The significant relationship between the Arctic Oscillation (AO) in December and the January climate over South China, Advances in Atmospheric Sciences, 28(2): 398-407.
33
YarAhmadi, D. and Azizi, A. (2008). Multivariate Analysis of Relationship Between Seasonal Rainfall in Iran with Climate Indices, Geographic research Quarterly, 62: 161-174.
34
Ye, H.; Fetzer, E.J.; Behrangi, A.; Wong, S.; Lambrigtsen, B.H.; Wang, C.Y. and Gamelin, B.L. (2016). Increasing daily precipitation intensity associated with warmer air temperatures over Northern Eurasia, Journal of Climate, 29(2): 623-636.
35
ORIGINAL_ARTICLE
Performance of Series Model CMIP5 in Simulation and Projection of Climatic Variables of Rainfall, Temperature and Wind Speed (Case Study: Yazd)
Introduction
According to the IPCC (2014) definition, climate change is a change in the state of the climate in which the average or its modifiable properties varies for a long period. To date various versions of climate change models have been presented for different purposes. These models are including (1) the assessment models of the Intergovernmental Panel on Climate Change (FAR), (2) the models entitled SAR, (3) the report model of TAR, and (4) the model report titled AR4 (CMIP3) and, finally, the 5th Assessment Report models, AR5 (CMIP5). These models use new emission scenarios called "RCP". These scenarios have four key lines called RCP2.6, RCP4.5, RCP6 and RCP8.5. General circulation models are considered as the most reliable tools for simulating climatic variables. These models can simulate the present climate and illustrate the conditions of the future climate under specific scenarios. Although these models are very helpful in the investigation and predictions based on future changes in climate, the outputs of these models are based on a large grid scale (250 to 600 km). Therefore, the application of these models is not suitable on a regional or local scale. The most important tool to create a bridge between a regional/local scale and GCM scales is downscaling. Among statistical downscaling methods, SDSM has been widely used for the downscaling of climate variables in the world. Most of the studies on climate change have been used in AR4 models. The majority of the studies in the word used AR5 models to investigate changes in climatic variables. As mentioned, most of the studies about climate change modeling have been performed using AR4 models. Therefore, studying and updating of that with CMIP5 data is necessary to reduce the uncertainty of modeling climate change in recent decades. Thus, in this study, three variables of rainfall, averaged temperature and maximum wind speed are modeled using AR5 models. These parameters are modeled according to the basic period of 1961-2005 and the future climate change will be simulated in a 95-year period from 2006 to 2100. The investigation on changes of the maximum wind speed in this region, as windy areas, is affected by dust storms every year. This can be of great importance in the studies of dust and wind erosion in the future.
Material and method
In this study, we use three types of data including daily rainfall, average temperature and maximum wind speed from synoptic station. Second sources of data are NCEP variables and the third are CanESM2 outputs.
For analysis of General Circulation Model, we used in this study the second generation of Canadian Earth System Model (CanESM2) developed by Canadian Centre for Climate Modeling and Analysis (CCCma) of Environment Canada. This is the only model that made daily predictor variables available to be directly fed into SDSM. Also this model has three scenarios such as RCP2.6, RCP4.5 and RCP8.5.
The model SDSM has four main parts including identification of predictors, model calibration, weather generator and generation of future series of climate variables.
Results and discussion
After reviewing data quality control, predictive variables were determined by NCEP. Calibration of the model was carried out using 70% of the observational data during the statistical period of 1961-1992 to determine the coefficients of the equation for modeling rainfall data, temperature and wind speed. The coefficients obtained in the calibration phase were used for 30% of the remaining data (1993-2005) for model verification. The performance of the SDSM was evaluated based on comparison of the results of verification and observational data in 1993-2005. The performance of the model on downscaling of temperature is higher than rainfall and wind speed. The reason for this is that the temperature parameter is continuous variable and there are no zero values.
The results of evaluation of performance and model uncertainty showed that NSE, RMSE, R2, PBIAS, RSR and Pearson correlation coefficient for mean temperature were 0.96, 1.64, 0.96, 1.63, 0.18, and 0.97 based on downscaled data by the NCEP and 0.88, 1.3, 0.91, 6.8, 0.33, 0.49 based on the downscaled data of CanESM2, which is of a fairly good value. The assessment criteria of rainfall and wind speed are less than the average temperature, which can be explained by the fact that the rainfall data and wind speed are not normal due to the presence of zero values. Based on the results of the Man-Kendall test, observation and modeled rainfall under two scenarios of RCP2.6 and RCP8.5 have no significant trend. While the data generated by the RCP4.5 scenario shows a significant decreasing trend. The results of the Mean-Kendall test on average showed that the series modeled by RCP scenarios, as well as observational data, have a significant incremental trend. Incremental trend of temperature indicates the existence of climate change in the study area. The maximum wind speed showed that the observation and modeled data by RCP2.6 and RCP4.5 scenarios had a significant decreasing trend, while the generated data based on the RCP8.5 scenario had an increasing trend.
Conclusion
Based on the results of CanESM2 model and SDSM, study area is not excluded from the climate change phenomenon. The trend model predicted an increase in average temperature in future under three RCP scenarios. The average temperature will increase by 1.54 degrees. According to the RCP2.6 scenario, the average temperature is 4.5 degrees, the scenario RCP4.5 is 7.7 degrees and the RCP8.5 scenario is 18.12 degrees higher than the base period. Unlike temperature variable, there is no significant change pattern for rainfall. According to RCP2.6 and RCP8.5 scenarios, rainfall will be increased in the future, while under RCP4.5 scenario, it will be decreased. The maximum wind speed will be increased by an average of 0.53 m/s compared with the base period. The RCP2.6 scenario is 4.9%, the scenario RCP4.5 44.4%, and the scenario RCP8.5 53.5% of increase compared with observational data.
https://jphgr.ut.ac.ir/article_69796_20eae33ffd9572bf3688bc8275ca1a3a.pdf
2018-09-23
593
609
10.22059/jphgr.2018.248177.1007156
Fifth Assessment Report
CanESM2
SDSM
statistical evaluation
trend
Maryam
Mirakbari
maryammirakbari@ut.ac.ir
1
PhD Candidate in Combating Desertification, Faculty of Natural Resources, University of Tehran, Iran
AUTHOR
Tayyebeh
Mesbahzadeh
tmesbah@ut.ac.ir
2
Assistant Professor of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Iran
LEAD_AUTHOR
Mohsen
Mohseni Saravi
msaravii@ut.ac.ir
3
Assistant Professor of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Iran
AUTHOR
Hasan
Khosravi
hkhosravi@ut.ac.ir
4
Professor of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Iran
AUTHOR
Ghasem
Mortezaie Farizhendi
mortezaiie@ut.ac.ir
5
Associate Professor of Development Studies, Iranian Academic Center for Education, Culture and Research (ACECR), Iran
AUTHOR
آقاخانی افشار، ا.؛ حسنزاده، ی.؛ بسالتپور، ع.؛ پوررضا بیلندی، م. (1395). ارزیابی سالیانة مؤلفههای اقلیمی حوضة آبخیز کشفرود در دورههای آتی با استفاده از گزارش پنجم هیئت بینالدول تغییر اقلیم، نشریة پژوهشهای حفاظت آب و خاک، 23(6): 217-233.
1
احمدوند کهریزی م. و روحانی ح. (1395). تأثیرات حفاظتی تیر اقلیم براساس ریزمقیاسسازی دمای پیشبینیشده در قرن 21 (مطالعة موردی: دو ایستگاه اراز کوسه و نوده در استان گلستان)، اکوهیدرولوژی، 3(6): 597-609.
2
برزگری، ف. و ملکینژاد، ح. (1395). بررسی و مقایسة تغییرات اقلیمی مناطق دشتی و کوهستانی در دورة 2010 تا 2030 (مطالعة موردی: حوضة آبخیز دشت یزد اردکان)، فیزیکوزمین، 42(1): 171-182.
3
جهانبخش اصل، س.؛ خورشیددوست، ع.؛ عالینژاد، م. و پوراصغر، ف. (1395). تأثیر تغییر اقلیم بر دما و بارش با درنظرگرفتن عدم قطعیت مدلها و سناریوهای اقلیمی، هیدروژئومورفولوژی، 7: 107-122.
4
صیاحی، ث.؛ شهبازی، ع. و خادمی، خ. (1395). پیشبینی اثر تغییر اقلیم بر رواناب ماهانة حوضة دزآب استفاده از مدل IHACRES، دوفصلنامة علوممهندسیآب، 15(7): 7-18.
5
نگارش، ح.؛ فلاح، ح. و خسروی، م. (1390). تجزیه و تحلیل ناهنجارهای اقلیمی مؤثر بر فرایند بیابانزایی در منطقة خضرآباد یزد، مجلة جغرافیا و برنامهریزی محیطی، ۳: 94-71.
6
Ahmadvand, M. and Rouhani, H. (2016). Climate change protection effects based on downscaling of the predicted temperature in the 21st century (case study: Araz Koseh and Navadeh in Golestan Province), Ecohydrology, 3(4): 597-609.
7
Alves, JMB.; Vasconcelos Junior, FC.; Chaves, RR.; Silva, EM.; Servain, J.; Costa, AA.; Sombra, SS. and Barbosa, ACB. (2016). Evaluation of the AR4 CMIP3 and the AR5 CMIP5 model and projections for precipitation in Northeast Brazil, Frontiers in Earth Science, 4(44): 1-22.
8
Aizen, E.M.; Aizen, V.B.; Melack, J.M.; Nakamura, T. and Ohta, T. (2001). Precipitation and atmospheric circulation patterns at mid-latitudes of Asia, International Journal of Climatology, 21(5): 535-556.
9
Almazroui, M.; Nazrul Islam, M., Saeed, F.; Alkhalaf, A. and Dambul, R. (2017). Assessing the robustness and uncertainties of projected changes in temperature and precipitation in AR5 Global Climate Models over the Arabian Peninsula, Atmospheric Research, 194: 202-213.
10
Aghakhani Afshar, A.; Hassanzadeh, Y.; Besalatpour, A. and Pourreza-Bilondi, M. (2017). Annual assessment of Kashafrood watershed basin climate components in future periods by using fifth report of intergovernmental panel on climate change, Water and Soil Conservation, 6: 217-233.
11
Barzegari, F. and Malekinejad, H. (2016). Prediction and comparison of Climate Changes in Mountainous and Palin Regions During 2010-2030 (Case Study: Yazd- Ardakan Watershed), Earth and Space Physics, 42(1): 171-182.
12
Dastorani, M.T.; Massah Bavani, A.R.; Poormohammadi, S. and Rahimian, M.H. (2011). Assessment of potential climate change impacts on drought indicators (Case study: Yazd station, Central Iran), Desert, 1: 159-167.
13
Dibike, Y.B. and Coulibaly, P. (2005). Hydrologic impact of climate change in the Saguenay watershed: Comparison of downscaling methods and hydrologic models, Hydrology, 307(1-4): 145-163.
14
Frey, K.E. and Smith, L.C. (2003). Recent temperature and precipitation increases in West Siberia and their association with the Arctic Oscillation. Polar Research, 22 (2): 287-300.
15
Fiseha, B.M.; Melesse, A.M.; Romano, E.; Volpi, E. and Fiori, A. (2012). Statistical Downscaling of Precipitation and Temperature for the Upper Tiber Basin in Central Italy, International Journal of Water Sciences, 1(3): 1-14.
16
Feng, S.; Hu, Q.; Huang, W.; Ho, C.H.; Li, R. and Tang, Z. (2014). Projected climate regime shift under future global warming from multi-model, multi-scenario CMIP5 simulations, Global and Planetary Change, 112: 41-52.
17
Gahanbakhsh Asl, S.; Khorshid Dost, A.; Ali Nejad, M. and Poor Asghar, F. (2017). He Impact of Climate Change on Temperature and Precipitation Considering the Uncertainty of Models and Climate Scenario, Hydrogeomorphology, 7: 107-122.
18
Gagnon, S.; Singh, B.; Rousselle, J. and Roy, L. (2005) An application of the statistical downscaling model (SDSM) to simulate climatic data for streamflow modelling in Québec, Canadian Water Resources, 30(4): 297-314.
19
Gebremeskel, S.; Liu, Y.B.; de Smedt, F.; Hoffmann, L. and Pfister, L. (2005). Analysing the effect of climate changes on streamflow using statistically downscaled GCM scenarios, International Journal River Basin Management, 2(4): 271-280.
20
Hassan, Z.; Shamsudin, S. and Harun, S. (2014). Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature, Theoretical and Applied Climatology, 116(1-2): 243-257.
21
Huang, J.; Zhang, J.; Zhang, Z.; Xu, C.; Wang, B. and Yao, J. (2011). Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method, Stochastic Environmental Research Risk Assessment, 25(6):781-792.
22
Hay, LE.; Wilby, RL. and Leavesley, GH. (2000). A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United StatesAmerican Water Resources Association, 36(2): 387-397.
23
IPCC (2013). Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, Cambridge.
24
IPCC (2014). Summary for policymakers, In: Climate Change 2014: Impacts, Adaptation, and Vulnerability, Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., astrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., Girma, B., Kissel, E.S., Levy, A.N., MacCracken, S., Mastrandrea, P.R., and White, L.L. (Eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA p. 1-32.
25
Kumar Bal, P.; Ramachandran, A.; Geetha, R.; Bhaskaran, B.; Thirumurugan, P.; Indumathi, J. and Jayanthi, N. (2016). Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a ownscaling approach using PRECIS, Theoretical Applied, Climatology, 123: 523-535.
26
Kharin, V.V.; Zwiers, F.W.; Zhang, X. and Wehner, M. (2013). Changes in temperature and precipitation extremes in the CMIP5 ensemble, Climate Change, 119: 345-357.
27
Liu, L.; Liu, Z.; Ren, X.; Fischer, T. and Xu, Y. (2011). Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model, Quaternary International, 244(2): 211-220.
28
Liu, Z.; Mehran, A.; Phillips, T.J.; Aghakouchak, A.; Res, C.; Liu, Z.; Mehran, A.; Phillips, T.J. and Aghakouchak, A. (2014). Seasonal and regional biases in CMIP5 precipitation simulations, Climate Research, 60(1): 35-50.
29
Miao, C.Y.; Duan, Q.Y.; Sun, Q.H. and Li, J.D. (2013). Evaluation and application of Bayesian multi-model estimation in temperature simulations, Progress in Physical Geograph, 37: 727-744.
30
Mahmood, R. and Babel, S.M. (2012). Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India, Theoretical and Applied Climatology, PP. 1-18.
31
Mehran, A.; Aghakouchak, A. and Phillips, T.J. (2014). Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations, Journal of Geophysical Research: Atmospheres, 119(4): 1695-1707.
32
Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; Van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T.; Meehl, G.A.; Mitchell, J.F.; Nalicenovic, N.; Riahi, K.; Smith, S.J.; Stouffer, R.J.; Thomson, A.M.; Weyant, J.P. and Wilbanks, T.J. (2010). The next generation of scenarios for climate change research and assessment, Journa of Nature, 463: 747-756.
33
Negaresh, H. and Khosravi, M. (2011). The Analysis of Climatical Abnormalities Influencing on Desertification Process in Khezer Abad Region of Yazd, Geography and Environmental Planning, 3: 71-79.
34
Nourein Mohammed, I.; Beverley, A. and Wemple, B. (2015). The use of CMIP5 data to simulate climate change impacts on flow regime within the Lake Champlain Basin, Journal of Hydrology: Regional Studies, 3: 160-186.
35
Pattnayak, K.C.; Kar, S.C.; Dalal, M. and Pattnayak, R. K. (2017). Projections of annual rainfall and surface temperature from CMIP5 models over the BIMSTEC countries, Global and Planetary Change, 152: 152-166.
36
Rui, Li and Geng, S. (2013). Impacts of climate change on agriculture and adaptive strategies in China, Integrative Agriculture, 12(8): 1402-1408
37
Sayahi, S.; Shahbazi, A. and Khademi, KH. (2016). Prediction of the effect of climate change on the monthly runoff of the basin Dez using IHACRES, Journal of Water Science Engineering, 15(7): 7-18.
38
Tabari, H.; Marofi, S.; Aeini, A.; HosseinzadehTalaeea, P. and Mohammadi, K. (2011). Trend analysis of reference evapotranspiration in the western half of Iran, Agricultural and Forest Meteorology, 151(2):128-136.
39
Tabari, H.; Abghari, H. and Hosseinzadeh Talaee, P. (2013). Impact of the North Atlantic Oscillation on stream flow in Western Iran, Hydrol, Process. DOI: 10.1002/hyp.9960.
40
Thomson, A.M.; Calvin, K.V.; Smith, S.J.; Kyle, G.P.; Volke, A.; Patel, P.; Delgado-Arias, S.; Bond-Lamberty, B.; Wise, M.A.; Clarke, L.E. and Edmonds, J.A. (2011). RCP4.5: A pathway for stabilization of radiative forcing by 2100, Climatic Change, 109(1): 77-94.
41
Van Vuuren, D.P.; Edmonds, J.; Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J. and Rose, S.K. (2011). The representative concentration pathways: An overview, Climatic Change, 109(1): 5-31.
42
Wilby, R.L.; Dawson, C.W. and Barrow, E.M. (2002). SDSM - A decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 17(2): 147-159.
43
Wetterhall, FA.; Bárdossy, D.; Chen, SH. and Xu, C-Y. (2006). Daily precipitation-downscaling techniques in three Chinese regions, Water Resources Research 42(aa):W11423.
44
Wilby, RL.; Whitehead, PG.; Wade, AJ.; Butterfield, D.; Davis, RJ. and Watts, G. (2006). Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK. Hydrology, 330(1-2): 204-220.
45
Xu, C.H. and Xu, Y. (2012). The Projection of Temperature and Precipitation over China under RCP Scenarios using a CMIP5 Multi-Model Ensemble, Atmospheric and Oceanic Science Letters, 5(6): 527-533.
46