بررسی اثر وجود منشأ کارست بر سهم جریان پایه رودخانه با استفاده از مدل اصلاح شده نواحی اشباع SAM (مطالعه موردی حوضه کازرون و دشت برم)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس

2 گروه مهندسی آب، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس، تهران، ایران

3 دانشیار دانشگاه تهران

چکیده

مدل‌های ماهانه بیلان آب (مدلهای بارش ـ رواناب در مقیاس ماهانه) از ابزارهای اصلی در برنامه ریزی بلند مدت منابع آب محسوب می‌شوند. ساختار اصلی این مدل‌ها شامل معادلات پیوستگی ذخیره رطوبتی خاک، جریان زیرسطحی و آب زیرزمینی است. با توجه به پیچیدگی فرایند شکل‌گیری و منشأهای متفاوت جریان، مدل‌های بارش ـ رواناب دارای ساختارهای متفاوت هستند که بر اساس شرایط حوضه آبریز مورد مطالعه نیاز به اصلاح، ساده سازی و بازنگری دارند. در صورت وجود منشأ کارستی در حوضه مطالعاتی، به دلیل اهمیت آن در تامین آب شرب نمی‌توان به سادگی آن را در مدلسازی نادیده گرفت. به دلیل پیچیدگی فرایند تشکیل رواناب با توجه به شرایط ساختاری زمین‌شناسی در حوضه‌های آبریز کارستی و اهمیت سازندهای کارستی، توسعه مدل‌های مفهومی و نزدیک کردن فرایند مدل به واقعیت فیزیکی حوضه اهمیت بسیاری دارد. در این تحقیق ساختار مدل مخزنی روزانه SAM برای بهبود پیش‌بینی جریان پایه و رواناب خروجی ماهانه در حوضه‌های کارستی کازرون و دشت برم اصلاح شده و سپس نتایج مدل اصلاح شده (SAM-KARST) با مدل اولیه (SAM) مقایسه و عملکرد مدل پیشنهادی ارزیابی شده است. نتایج بهبود نسبی شاخص‌های عملکردی (حدود 10 درصد) مدل SAM-KARST در مقایسه با مدل SAM را برای حوضه آبریز مطالعاتی نشان داد. با در نظر گرفتن مخزن مفهومی برای منشأ کارست در مدل، میزان سهم جریان پایه به صورت بارز افزایش و به بالای 70 درصد رسید که آن نشان دهنده نقش مهم منشأ کارست در تامین جریان پایه می‌باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

A study on the effects of karst area on the source of river’s base flow using modified Saturation Area Model (SAM) in Kazeroon and Barm Plain basins

نویسندگان [English]

  • Shayan Mohseni Bileh Savarchi 1
  • Farzin Nasiri Saleh 2
  • Banafsheh Zahraie 3
1 Department of water Engineering, Faculty of Civil and Environmental Engineering, Tarbiat Modares University
2 Department of Water Engineering, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
3 Tehran University
چکیده [English]

Monthly water balance models (rainfall-runoff models on a monthly basis) are major tools in long-term planning of water resources. The basic structure of these models includes continuity equations for soil moisture storage, subsurface flow and groundwater. Due to the complexity of the formation process and the different sources of flow, Rainfall-runoff models have different structures and need to be improved, simplified and revised according to the studied catchment conditions. If there is a karst origin in the study area, because of its importance in providing drinking water, it can not be simply ignored in the modeling. In this study, the structure of daily reservoir Saturation Area Model (SAM) is modified to improve the prediction of base-flow and monthly runoff in Kazeroon and Barm plain karst basins. The results of the modified model (SAM-KARST( and original model (SAM) have been compared and then the performance of the proposed model has been evaluated. The obtained results showed a relative improvement in the performance parameters of SAM-KARST in comparison to SAM for the study basins.
By considering the conceptual reservoir for karst origin in the model, the contribution of base flow obviously increased, which indicate the important role of karst origin in supplying base flow.

کلیدواژه‌ها [English]

  • Saturation Area Model
  • Monthly Water Balance
  • Kazeroon basin
  • Karst
  • Base-flow
Alley WM (1984) On the treatment of evapotranspiration, soil moisture accounting, and aquifer recharge in monthly water balance models. Water Resources Research 20(8):1137-1149
Birkel C, Soulsby C (2016) Linking tracers, water age and conceptual models to identify dominant runoff processes in a sparsely monitored humid tropical catchment. Hydrological Processes 30(24):4477-4493
Birkel C, Soulsby C, Tetzlaff D (2015) Conceptual modelling to assess how the interplay of hydrological connectivity, catchment storage and tracer dynamics controls nonstationary water age estimates. Hydrological Processes 29(13):2956-2969
Birkel C, Tetzlaff D, Dunn SM, Soulsby C (2010) Towards a simple dynamic process conceptualization in rainfall–runoff models using multi‐criteria calibration and tracers in temperate, upland catchments. Hydrological Processes: An International Journal 24(3): 260-275
Birkel C, Tetzlaff D, Dunn SM, Soulsby C (2010) Towards a simple dynamic process conceptualization in rainfall–runoff models using multi‐criteria calibration and tracers in temperate, upland catchments. Hydrological Processes: An International Journal 24(3):260-275
Birkel C, Tetzlaff D, Dunn SM, Soulsby C (2011) Using time domain and geographic source tracers to conceptualize streamflow generation processes in lumped rainfall‐runoff models. Water Resources Research 47(2)
Criss RE, Winston WE (2008) Do Nash values have value? Discussion and alternate proposals. Hydrological Processes: An International Journal 22(14):2723-2725
Eini M R, Javadi S, and Delavar M (2019) Development of comprehensive Karstic watershed model in order to make estimates and precision for the components of the water balance. Iran-Water Resources Research 14(5):125-136 (In Persian)
Fenicia F, McDonnell JJ, Savenije HH (2008) Learning from model improvement: On the contribution of complementary data to process understanding. Water Resources Research 44(6)
Ford D (2007) Jovan Cvijić and the founding of karst geomorphology. Environmental Geology 51(5):675-684
Hamon WR (1963) Computation of direct runoff amounts from storm rainfall. International Association of Scientific Hydrology Publication 63:52-62
Kampf SK, Burges SJ (2010) Quantifying the water balance in a planar hillslope plot: Effects of measurement errors on flow prediction. Journal of Hydrology 380(1-2):191-202
Kirchner JW (2006) Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resources Research 42(3)
Kirchner JW (2009) Catchments as simple dynamical systems: Catchment characterization, rainfall‐runoff modeling, and doing hydrology backward. Water Resources Research 45(2)
Lischeid G (2008) Combining hydrometric and hydrochemical data sets for investigating runoff generation processes: tautologies, inconsistencies and possible explanations. Geography Compass 2(1):255-280
Malcolm IA, Soulsby C, Youngson AF, Hannah DM, McLaren IS, Thorne A (2004) Hydrological influences on hyporheic water quality: implications for salmon egg survival. Hydrological Processes 18(9):1543-1560
McCabe GJ, Markstrom SL (2007) A monthly water-balance model driven by a graphical user interface (No. 2007-1088). Geological Survey (US)
Moghimi H (2012) Applied hydrology. Payam Noor University Press, 261p (In Persian)
Nash JE (1970) River flow forecasting through conceptual models, I: A discussion of principles. Journal of Hydrology 10:398-409
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology 10(3):282-290
Palanisamy B, Workman SR (2015) Hydrologic modeling of flow through sinkholes located in streambeds of Cane Run Stream, Kentucky. Journal of Hydrologic Engineering 20(5):04014066
Peranginangin N, Sakthivadivel R, Scott NR, Kendy E, Steenhuis TS (2004) Water accounting for conjunctive groundwater/surface water management: Case of the Singkarak–Ombilin River basin, Indonesia. Journal of Hydrology 292(1-4):1-22
Rahnama B, Naseri M, and Zahraie B (2014) Identifying optimized structure and uncertainty analysis of monthly water balance model. Iranian Water Researches Journal 8(14):77-86 (In Persian)
Savenije HH (2009) HESS opinions “The art of hydrology”. Hydrological and Earth System Science 13(2): 157–161
Seibert J, McDonnell JJ (2002) On the dialog between experimentalist and modeler in catchment hydrology: Use of soft data for multicriteria model calibration. Water Resources Research 38(11):23-1-23-14
Soulsby C, Birkel C, Geris J, Dick J, Tunaley C, Tetzlaff D (2015) Stream water age distributions controlled by storage dynamics and nonlinear hydrologic connectivity: Modeling with high‐resolution isotope data. Water Resources Research 51(9):7759-7776
Soulsby C, Rodgers PJ, Petry J, Hannah DM, Malcolm IA, Dunn SM (2004) Using tracers to upscale flow path understanding in mesoscale mountainous catchments: Two examples from Scotland. Journal of Hydrology 291(3-4):174-196
Soulsby C, Tetzlaff D, Van den Bedem N, Malcolm IA, Bacon PJ, Youngson AF (2007) Inferring groundwater influences on surface water in montane catchments from hydrochemical surveys of springs and streamwaters. Journal of Hydrology 333(2-4):199-213
Spruill CA, Workman SR, Taraba JL (2000) Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Transactions of the ASAE 43(6):1431
Tetzlaff D, Birkel C, Dick J, Geris J, Soulsby C (2014) Storage dynamics in hydropedological units control hillslope connectivity, runoff generation, and the evolution of catchment transit time distributions. Water Resources Research 50(2):969-985
Tetzlaff D, McDonnell JJ, Uhlenbrook S, McGuire KJ, Bogaart PW, Naef F, Baird AJ, Dunn SM, Soulsby C (2008) Conceptualizing catchment processes: simply too complex?. Hydrological Processes 22(11):1727-1730
Xu CY, Singh VP (1998) A review on monthly water balance models for water resources investigations. Water Resources Management 12(1):20-50
Zhang L, Potter N, Hickel K, Zhang Y, Shao Q (2008) Water balance modeling over variable time scales based on the Budyko framework–Model development and testing. Journal of Hydrology 360(1-4):117-131
Zhang Z, Chen X, Cheng Q, Soulsby C (2019) Storage dynamics, hydrological connectivity and flux ages in a karst catchment: Conceptual modelling using stable isotopes. Hydrology and Earth System Sciences 23(1):51-71
Zhang Z, Chen X, Soulsby C (2017) Catchment‐scale conceptual modelling of water and solute transport in the dual flow system of the karst critical zone. Hydrological Processes 31(19):3421-3436