آشکارسازی و نسبت‌دهی اثرات تغییر اقلیم بر رواناب ورودی به سد کرج در دوره‌های گذشته

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه تهران، پردیس ابوریحان، دانشکده مهندسی آبیاری و زهکشی، گروه مهندسی منابع آب

2 پردیس ابوریحان دانشگاه تهران

3 شرکت آب منطقه‌ای البرز

4 گروه مهندسی منابع آب، پردیس ابوریحان، دانشگاه تهران

چکیده

 
یکی از مهم­ترین پیامدهای تغییر اقلیم و گرمایش جهانی، تأثیر متغیرهای اقلیمی بر متغیرهای هیدرولوژیکی و منابع آب است. سد کرج به عنوان یکی از منابع مهم آب و برق استان­های تهران و البرز از اهمیت ویژه­ای برخوردار است. لذا شناسایی عوامل مؤثر بر منابع آب حوضه بالادست این سد و رواناب ورودی به آن می­تواند در برنامه­ریزی­های آتی و مدیریت منابع آب این نقطه از کشور مؤثر باشد. یکی از روش­های شناسایی اثرات تغییر اقلیم بر متغیرهای هواشناسی و هیدرولوژیکی، آشکارسازی و نسبت­دهی با روش انگشت نگاشت بهینه است. در این تحقیق سعی شد تا با استفاده از سیگنال­های ALL، GHG و NAT مستخرج از مدل CanESM-2.0 و ریزمقیاس­نمایی آنها با رویکرد MRQNBC و رواناب ناشی از متغیرهای اقلیمی برای دوره آماری (2011-1985) به کمک مدل کالیبره شده SWAT مدل‎سازی شود تا به وسیله انگشت نگاشت بهینه، اثرات هر یک از سیگنال­ها، آشکارسازی و نسبت­دهی گردد. با توجه به نتایج به دست آمده تنها سیگنال GHG که معرف شرایط جوی کره زمین تحت تأثیر گازهای گلخانه­ای (بدون لحاظ کردن سایر عوامل) می­باشد، آشکارسازی و نسبت­دهی شد و ضریب مقیاس‌ساز (β) آن 76/0 به دست آمد. در حالی که دو سیگنال دیگر ALL و NAT که به ترتیب جهان را تحت شرایط عادی امروزی و شرایط صرفاً طبیعی (بدون لحاظ نمودن سایر عوامل) شبیه­سازی نمودند، قابل آشکارسازی و نسبت­دهی نبودند. به عبارت دیگر تغییرات رواناب ورودی به سد کرج تنها با رواناب شبیه­سازی شده تحت تأثیر گازهای گلخانه­ای (به تنهایی) هماهنگی داشت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Detection and Attribution of Climate Change Effects on Entered Runoff to Karaj Dam in the past periods

نویسندگان [English]

  • Erfan Naseri 1
  • Tofigh Saadi 3
  • Saman Javadi 4
1 Department of Irrigation and Drainage Engineering, Aburaihan College of University of Tehran, Pakdasht, Iran
2
3 Regional Water Company of Alborz, Mehrshahr, Karaj, Iran
4 Department of Water Resources, College of Aburaihan, University of Tehran
چکیده [English]

One of the most important consequences of Climate Change is the changing of climatic variables and the effects on hydrological variables and water resources. Karaj Dam is one of the important sources of water and electrical energy for Tehran and Alborz province. Thus, diagnosis of affective factors on the water resources of upstream of this catchment and entered runoff to Karaj dam could be vital for planners and policy makers for the future periods. One of the best methods for diagnosing the effects of Climate Change on climatic and hydrological variables is Detection and Attribution with Optimal Fingerprint approach. In this research have been tried to detect and attribute the ALL, GHG and NAT signals by simulations from CanESM-2.0 model. Therefor every signal has been downscaled with MRQNBC method, and then simulated with SWAT calibrated model for selected signals for (1985-2011). At last those entered to optimal fingerprint method. By attention the results, just GHG signal (which shows the globe under the effects of Greenhouse gases without the other forcing has been detected and attributed and its scaling factor (β) got 0.76. While two other signals (ALL, NAT) haven’t been detected and attributed. In other words the changing of entered runoff to Karaj dam was very likely consonant with simulated runoff which affected from GHG signal only.

کلیدواژه‌ها [English]

  • Detection
  • Optimal Fingerprint
  • SWAT
  • MRQNBC
  • Karaj Dam
Abbaspour KC (2007) A user manual of SWAT–CUP, SWAT calibration and uncertainty programs. Eawag: Swiss Federal Institute of Aquatic and Science and Technology, 100p
Abbaspour KC, Faramarzi M, Ghasemi SS, and Yang H (2009) Assessing the impact of climate change on water resources in Iran. Water Resources Research 45(10):1–16
Allen MR and Stott PA (2003) Estimating signal amplitudes in optimal fingerprinting, part I: Theory. Climate Dynamics 21(5–6):477–491
Allen MR and Tett SFB (1999) Checking for model consistency in optimal fingerprinting. Climate Dynamics 15(6):419–434
Cramer W, Yohe GW, Auffhammer M, Huggel C, Molau U, Da Silva Dias MAF, Solow A, Stone DA, Tibig L, Leemans R, … Hansen G (2015) Detection and attribution of observed impacts. Climate Change 2014 Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects 979–1038
Forbes WL, Mao J, Jin M, Kao SC, Fu W, Shi X, Riccuito DM, Thornton PE, Ribes A, Wang Y, … Hayes DJ (2018) Contribution of environmental forcings to US runoff changes for the period 1950-2010. Environmental Research Letters 13(5):054023
Hannart A, Ribes A, and Naveau P (2014) Optimal fingerprinting under multiple sources of uncertainty. Geophysical Research Letters 41(4):1261–1268
Hasselmann K (1997) Multi-pattern fingerprint method for detection and attribution of climate change. Climate Dynamics 13(9):601–611
Hasselmann K, Bengtsson L, Cubasch U, Hegerl GC, Rodhe H, Roeckner E, Storch HV, Voss R, and Waszkewitz J (1995) Detection of anthropogenic climate change using a fingerprint method. Max-Planck-Institut fur Meteorologie (168)
Hegerl G and Zwiers F (2011) Use of models in detection and attribution of climate change. Wiley Interdisciplinary Reviews: Climate Change 2(4):570–591
Johnson F and Sharma A (2012) A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resources Research 48(1):1–16
Krause P, Boyle DP, and Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences 5(5):89–97
Krysanova V and Arnold JG (2008) Advances in ecohydrological modelling with SWAT-A review. Hydrological Sciences Journal 53(5):939–947
Li H, Sheffield J, and Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. Journal of Geophysical Research Atmospheres 115(10)
Mehrotra R, Johnson F, and Sharma A (2018) A software toolkit for correcting systematic biases in climate model simulations. Environmental Modelling and Software 104:130–152
Mehrotra R and Sharma A (2012) An improved standardization procedure to remove systematic low frequency variability biases in GCM simulations. Water Resources Research 48(12):1-8
Mehrotra R and Sharma A (2015) Correcting for systematic biases in multiple raw GCM variables across a range of timescales. Journal of Hydrology 520:214–223
Mehrotra R and Sharma A (2016) A multivariate quantile-matching bias correction approach with auto- and cross-dependence across multiple time scales: Implications for downscaling. Journal of Climate 29(10):3519–3539
Mehrotra R and Sharma A (2019) A resampling approach for correcting systematic spatiotemporal biases for multiple variables in a changing climate. Water Resources Research 55(1):754–770
Mondal A and Mujumdar PP (2012) On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow. Water Resources Research 48(10):1–18
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD and Veith TL (2007) Model evaluation guidelines for systematic quantification  of accuracy in watershed simulations.Transactions of the ASABE 50(3):885−900
Naseri E, Massah Bavani A, and Saadi Tofigh (2019) Evaluating the efficiency of GCM models for estimating the average temperature of Alborz province in (1985-2015) historical Period. 6th International-Regional Conference on Climate Change, 18-19 November, National Library of Iran, Tehran, Iran (In Persian) 
Naseri E, Shahidi A, and Farzaneh MR (2015) The assesment of climate change on run-off by SWAT model. Iranian Journal of Rainwater Catchment Systems 3(9):27-38 (In Persian)
Neitsth SL, Arnold JG, Kiniry JR, and Williams JR (2005) Soil and water assessment tools theorical documentation. Grassland, Soil and Water Research Laboratory, Agricultural research service, 494p
Ribes A, Azaís JM, and Planton S (2009) Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate. Climate Dynamics 33(5):707–722
Ribes A, Planton S, and Terray L (2013) Application of regularised optimal fingerprinting to attribution. Part I: Method, properties and idealised analysis. Climate Dynamics 41(11–12):2817–2836
Ribes A, Zwiers FW, Azaïs JM, and Naveau P (2017) A new statistical approach to climate change detection and attribution. Climate Dynamics. Springer Berlin Heidelberg 48(1–2):367–386
Saadi T, Alijani B, Akbari M, and Massah Bavani A (2016) Detection of extreme precipitation changes and attribution to climate change using standard optimal fingerprinting (Case study: The Southwest of Iran). Journal of Spatial AnalysisEnvironmental Hazards 3(3):65-80(In Persian)
Scinocca JF, Kharin V V, Jiao Y, Qian MW, Lazare M, Solheim L, Flato GM, Biner S, Desgagne M, and Dugas B (2016) Coordinated global and regional climate modeling. Journal of Climate 29(1):17–35
Shim S, Kim J, Yum SS, Lee H, Boo KO, and Byun YH (2019) Effects of anthropogenic and natural forcings on the summer temperature variations in East Asia during the 20th century. Atmosphere 10(11):690
Shirazi M, Naseri M, and Zahraie B (2018) Detection and attribution of exterme precipitation in Iran. The 6th Comprhensive Conference on Flood Engineering and Management, Tehran, Iran (In Persian)
Velasquez P, Messmer M, and Raible C (2019) A new bias-correction method for precipitation over complex terrain suitable for different climate states. Geoscientific Model Development Discussions (July):1–27
Wang Z, Jiang Y, Wan H, Yan J, and Zhang X (2020) Toward optimal fingerprinting in detection and attribution of changes in climate extremes. Journal of the American Statistical Association 1–23, DOI: 10.1080/01621459.2020.1730852
Yuemei H, Xiaoqin Z, Jianguo S, and Jina N (2008) Conduction between left superior pulmonary vein and left atria and atria fibrillation under cervical vagal trunk stimulation. Colombia Medica 39(3):227–234
Zare Garizi A and Talebi A (2017) Water balance simulation for the Ghare-Sou Watershed, Golestan, using the SWAT model. Journal Management System 9(30):37-50 (In Persian)
Zhang X, Wan H, Zwiers FW, Hegerl GC, and Min SK (2013) Attributing intensification of precipitation extremes to human influence. Geophysical Research Letters 40(19):5252–5257
Zohrabi N, Massah Bavani A, Goodarzi E, and Eslamian S (2014) Attribution of temperature and precipitation changes to greenhouse gases in northwest Iran. Quaternary International. Elsevier Ltd and INQUA 345:130–137