تعیین سهم نسبی مؤلفه های تشکیل‌دهنده رواناب سالانه به کمک مدل ردیابی آب و توابع زمان پیمایش: مطالعه موردی حوضه آبریز گدارچای واقع در جنوب غرب دریاچه ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد مهندسی عمران-گرایش مهندسی منابع آب، گروه مهندسی آب و محیطزیست، دانشکده مهندسی عمران، دانشگاه صنعتی شریف.

2 استادیار گروه مهندسی آب و محیطزیست، دانشکده مهندسی عمران، دانشگاه صنعتی شریف.

چکیده

تعیین سهم هر یک از فرایندهای مؤثر در تشکیل جریان رودخانه، نقش مهمی در تدقیق شبیه‌­سازی این فرایندها در مدل­‌سازی هیدرولوژیکی حوضه­ آبریز دارد. اگرچه بیلان آب دید کلی از تغییرات حجمی مؤلفه‌­های تشکیل‌دهنده­ آن به‌دست می‌­دهد، منشأیابی شارهای خروجی از حوضه و تغییرات زمانی آنها نیازمند بکارگیری رویکردهای مبتنی بر ردیابی آب است. در این پژوهش با استفاده از توسعه­ یک مدل فیزیکی و یکپارچه­ هیدرولوژیکی سه بُعدی با قابلیت ردیابی آب (ParFlow-CLM-EcoSLIM)، میزان سهم هر یک از مؤلفه­‌های تشکیل دهنده­ جریان مانند آب حاصل از باران، آب حاصل از برف و آب زیرزمینی در تشکیل جریان رودخانه و تبخیر-تعرق بررسی شده است. همچنین، توزیع زمان پیمایش جریان‌های خروجی شبیه­‌سازی و مطالعه شده است. به منظور تبیین مدل­‌های توسعه داده شده، حوضه آبریز گدارچای واقع در جنوب غرب دریاچه ارومیه به عنوان منطقه­ مطالعه انتخاب شد. نتایج نشان داد که ذوب برف، منشأ اصلی دبی رودخانه و تبخیر-تعرق در منطقه است، به طوری که سهم برف، باران و آب زیرزمینی در تولید جریان رودخانه در طول سال آبی به طور متوسط به ترتیب برابر با 56، 5 و 39 درصد است. همچنین، در حدود 55 درصد از برف ورودی در طول سال آبی پس از فرایند ذوب و نفوذ در خاک  ذخیره می‌شود و در سال‌های بعد از سیستم خارج می‌شود؛ در حالیکه تنها 22 درصد از باران جدید در سیستم ذخیره می‌شود و 78 درصد از آن در همان سال آبی از سیستم خارج می‌شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Determining the Relative Contribution of Annual Runoff Components using a Water Tracking Model and Travel Time Distributions: The Case of Godarchay Basin in Southwestern of Lake Urmia

نویسندگان [English]

  • Javad Rahmani 1
  • Mohammad Danesh Yazdi 2
1 M.Sc. in Water Resources Management, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.
2 Assistant Professor, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.
چکیده [English]

Determining the contribution of each of the effective processes to river flow plays an important role in how accurate these processes are estimated via hydrological modeling. Although water balance provides an overview of the volumetric changes of its components, the origin of the outflows and their temporal changes require utilization of approaches based on water tracking. In this study, we developed a three-dimensional physical and integrated hydrological model and coupled it to a water tracking model (ParFlow-CLM-EcoSLIM) to determine the contribution of rainfall, snowmelt, and groundwater to streamflow and evapotranspiration. Also, the distribution of the travel time of outfluxes has been simulated and studied. To demonstrate the applicability of the developed models, we chose the Godarchay basin, located in the southwest of Lake Urmia, as the study area. The results showed that snowmelt is the main source of river discharge and evapotranspiration in the region, such that the average contribution of snowmelt, rainfall and groundwater to the runoff production during a water year, is equal to 56%, 5% and 39%, respectively. Also, about 55% of the incoming snow during a water year is stored in the soil after the process of melting and infiltration, and is then removed from the system in the following years; while only 22% of new rain is stored in the system and 78% of it leaves the system in the same year.

کلیدواژه‌ها [English]

  • Hydrological Modeling
  • Particle Tracking
  • Gadarchay Basin
  • Snowmelt
  • Travel Time Distribution
Benettin P, Kirchner JW, Rinaldo A, Botter G (2015a) Modeling chloride transport using travel time distributions at Plynlimon, Wales. Water Resources Research 51(5):3259–3276
Benettin P, Rinaldo A, Botter G (2015b) Tracking residence times in hydrological systems: Forward and backward formulations. Hydrological Process 29(25):5203–5213
Berry ZC, Evaristo J, Moore G, Poca M, Steppe K, Verrot L, Asbjornsen H, Borma LS, Bretfeld M, Hervé-Fernández P, Seyfried M, Schwendenmann L, Sinacore K, De Wispelaere L, McDonnell J (2018) The two water worlds hypothesis: Addressing multiple working hypotheses and proposing a way forward. Ecohydrology 11(3):1–10
Birkel C, Soulsby C (2015) Advancing tracer-aided rainfall–runoff modelling: A review of progress, problems and unrealised potential. Hydrological Processes 29(25):5227–5240
Botter G, Bertuzzo E, Rinaldo A (2011) Catchment residence and travel time distributions: The master equation. Geophysical Research Letters 38(11):1–6
Botter G, Bertuzzo E, Rinaldo A (2010) Transport in the hydrologic response: Travel time distributions, soil moisture dynamics, and the old water paradox. Water Resources Research 46(3):1–18
Chan F, Tiwari M (2007) Swarm Intelligence: Focus on ant and particle swarm optimization. BoD–Books on Demand
Dai Y, Zeng X, Dickinson RE, Baker I, Bonan GB, Bosilovich MG, Denning AS, Dirmeyer PA, Houser PR, Niu G (2003) The common land model. Bulletin of the American Meteorological Society 84(8):1013-1024
Danesh-Yazdi M, Klaus J, Condon LE, Maxwell RM (2018) Bridging the gap between numerical solutions of travel time distributions and analytical storage selection functions. Hydrological Processes 32(8):1063–1076
Danesh-Yazdi M, Foufoula-Georgiou E, Karwan DL, Botter G (2016) Inferring changes in water cycle dynamics of intensively managed landscapes via the theory of time-variant travel time distributions. Water Resources Research 52(10):7593–7614
Harman CJ (2015) Time-variable transit time distributions and transport: Theory and application to storage‐dependent transport of chloride in a watershed. Water Resources Research 51(1):1–30
Hessari B, Yousefi P, Alinia M (2019) Comparing the effects of different filtering formulas on base flow separation based on daily flow data (Case study: West Rivers of Urmia Lake). Iranian journal of Ecohydrology 6(2):305–321
Hrachowitz M, Savenije H, Bogaard TA, Tetzlaff D, Soulsby C (2013) What can flux tracking teach us about water age distribution patterns and their temporal dynamics? Hydrology and Earth System Sciences 17(2):533-564
Japan International Cooperation Agency (2020) Data collection survey on the improvement of hydrological cycle model of Lake Urmia Basin In the Islamic Republic of Iran. Final Report (Issue June)
Jones JE, Woodward CS (2001) Newton-Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems. Advances in Water Resources 24(7):763-774
Kollet SJ, Maxwell RM (2008) Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resources Research 44(2):1–18
Kollet SJ, Maxwell RM (2006) Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model. Advances in Water Resources 29(7):945-958
Land-Atmosphere Interaction Research Group at Sun Yat-sen University (2021) (http:// globalchange.bnu.edu.cn/research/soil4.jsp)
Maxwell RM (2013) A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling. Advances in Water Resources 53:109–117
Maxwell RM, Condon LE, Danesh-Yazdi M, Bearup LA (2019) Exploring source water mixing and transient residence time distributions of outflow and evapotranspiration with an integrated hydrologic model and Lagrangian particle tracking approach. Ecohydrology 12(1):1–10
Maxwell RM, Miller NL (2005) Development of a coupled land surface and groundwater model. Journal of Hydrometeorology 6(3):233-247
McGuire KJ, McDonnell JJ (2006) A review and evaluation of catchment transit time modeling. Journal of Hydrology 330(3-4):543-563
Montzka C, Herbst M, Weihermüller L, Verhoef A, Vereecken H (2017) A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves, link to model result fles in NetCDF format. PANGAEA,. https://doi.org/10.1594/PANGAEA.870605. In Supplement to: Montzka C et al. (2017): A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves. Earth System Science Data 9(2):529-543
Osei-Kuffuor D, Maxwell RM, Woodward CS (2014) Improved numerical solvers for implicit coupling of subsurface and overland flow. Advances in Water Resources 74:185-195
Rahmani J, Danesh-Yazdi M (2022) Quantifying the impacts of agricultural alteration and climate change on the water cycle dynamics in a headwater catchment of Lake Urmia Basin. Agricultural Water Management 270:107749
Remondi F, Botter M, Burlando P, Fatichi S (2019) Variability of transit time distributions with climate and topography: A modelling approach. Journal of Hydrology 569:37-50
Sprenger M, Seeger S, Blume T, Weiler M (2016) Travel times in the vadose zone: Variability in space and time. Water Resources Research 52(8):5727–5754
van Der Velde Y, Heidbüchel I, Lyon SW, Nyberg L, Rodhe A, Bishop K, Troch PA (2015) Consequences of mixing assumptions for time‐variable travel time distributions. Hydrological Processes 29(16):3460-3474
Van Der Velde Y, Torfs P, Van Der Zee S, Uijlenhoet R (2012) Quantifying catchment-scale mixing and its effect on time-varying travel time distributions. Water Resources Research 48(6)