Refrences:
1.Duan, Q., Gupta, V.K., and Sorooshian, S. Shu_ed complex evolution approach for e_ective and e_cient global minimization", Journal of Optimization Theory and Applications, 76(3), pp. 501-521 (1993).
2. Gupta, H.V., Sorooshian, S., and Yapo, P.O. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration", Journal of Hydrologic Engineering, 4(2), pp. 135-143 (1999).
3. Johnston, P. and Pilgrim, D. Parameter optimization for watershed models", Water Resources Research, 12(3), pp. 477-486 (1976).
4. Kolda, T.G., Lewis, R.M., and Torczon, V. Optimization by direct search: New perspectives on some classical and modern methods", SIAM Review, 45(3), pp. 385-482 (2003). 5. Hendrickson, J., Sorooshian, S., and Brazil, L.E. Comparison of Newton-type and direct search algorithms for calibration of conceptual rainfall-runo_ models", Water Resources Research, 24(5), pp. 691- 700 (1988). 6. Duan, Q., Sorooshian, S., and Gupta, V. E_ective and e_cient global optimization for conceptual rainfall-runo_ models", Water Resources Research, 28(4), pp. 1015-1031 (1992). 7. Naeini, M.R., Yang, T., Sadegh, M., et al. Shu_ed complex-self adaptive hybrid evolution (SC-SAHEL) optimization framework", Environmental Modelling & Software, 104, pp. 215-235 (2018). 8. Nelder, J.A. and Mead, R. A simplex method for function minimization", The Computer Journal, 7(4), pp. 308-313 (1965). 9. Price, W. Global optimization algorithms for a CAD workstation", Journal of Optimization Theory and Applications, 55(1), pp. 133-146 (1987). 10. Holland, J.H., Adaptation in Natural and Arti_cial Systems: An Introductory Analysis with Applications to Biology, Control, and Arti_cial Intelligence, MIT press (1992). 11. Duan, Q., Sorooshian, S., and Gupta, V.K. Optimal use of the SCE-UA global optimization method for calibrating watershed models", Journal of Hydrology, 158(3-4), pp. 265-284 (1994). 12. Eusu_, M., Lansey, K., and Pasha, F. Shu_ed frog-leaping algorithm: a memetic metaheuristic 2024 M. Rahnamay Naeini et al./Scientia Iranica, Transactions A: Civil Engineering 26 (2019) 2015{2031 for discrete optimization", Engineering Optimization, 38(2), pp. 129-154 (2006). 13. Eberhart, R. and Kennedy, J. A new optimizer using particle swarm theory. In MHS'95", Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, pp. 39-43 (1995). 14. Mariani, V.C. Luvizotto, L.G.J., Guerra, F.A., et al. A hybrid shu_ed complex evolution approach based on di_erential evolution for unconstrained optimization", Applied Mathematics and Computation, 217(12), pp. 5822-5829 (2011). 15. Chu, W., Gao, X., and Sorooshian, S. A new evolutionary search strategy for global optimization of high-dimensional problems", Information Sciences, 181(22), pp. 4909-4927 (2011). 16. Yapo, P.O., Gupta, H.V., and Sorooshian, S. Multiobjective global optimization for hydrologic models", Journal of Hydrology, 204(1-4), pp. 83-97 (1998). 17. Hadka, D. and Reed, P. Borg: An auto-adaptive many-objective evolutionary computing framework", Evolutionary Computation, 21(2), pp. 231-259 (2013). 18. Deb, K., Pratap, A., Agarwal, S., et al. A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197 (2002). 19. Goldberg. D.E., Genetic Algorithms in Addison Wesley Search, Optimization, and Machine Learning, Addison Wesley (1989). 20. Yang, T., Gao, X., Sellars, S.L., et al. Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex", Environmental Modelling & Software, 69, pp. 262-279 (2015). 21. Guo, J., Zhou, J., Zou, Q., et al. A novel multiobjective shu_ed complex di_erential evolution algorithm with application to hydrological model parameter optimization", Water Resources Management, 27(8), pp. 2923-2946 (2013). 22. Rahimi-Vahed, A. and Mirzaei, A.H. A hybrid multiobjective shu_ed frog-leaping algorithm for a mixedmodel assembly line sequencing problem", Computers & Industrial Engineering, 53(4), pp. 642-666 (2007). 23. Gilks, W., Richardson, S., and Spiegelhalter, D., Practical Markov Chain Monte Carlo, New York., Chapman-Hall (1996). 24. Vrugt, J.A., Gupta, H.V., Bouten, W., et al. A shu_ed complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters",Water Resources Research, 39(8), 1201 (2003). 25. Vrugt, J.A., Gupta, H.V., Bastidas, L.A., et al. E_ective and e_cient algorithm for multiobjective optimization of hydrologic models", Water Resources Research, 39(8), 1214 (2003). 26. Zitzler, E. and Thiele, L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach", IEEE Transactions on Evolutionary Computation, 3(4), pp. 257-271 (1999). 27. Vrugt, J.A., Ter Braak, C., Diks, C., et al. Accelerating Markov chain Monte Carlo simulation by di_erential evolution with self-adaptive randomized subspace sampling", International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), pp. 273-290 (2009). 28. Hinnell, A.C., Ferr_e, T.P.A., Vrugt, J.A., Huisman, J.A., Moysey, S., Rings, J., and Kowalsky, M.B. Improved extraction of hydrologic information from geophysical data through coupled hydrogeophysical inversion", Water Resour. Res., 46, W00D40 (2010). 29. Moradkhani, H. and Sorooshian, S. General review of rainfall-runo_ modeling: model calibration, data assimilation, and uncertainty analysis", In Hydrological Modelling and the Water Cycle, Springer, pp. 1-24 (2009). 30. Gupta, V.K. and Sorooshian, S. The relationship between data and the precision of parameter estimates of hydrologic models", Journal of Hydrology, 81(1-2), pp. 57-77 (1985). 31. Burnash, R.J., Ferral, R.L., and McGuire, R.A., A Generalized Streamow Simulation System Conceptual Modeling for Digital Computers, U.S. Dept. of Commerce, National Weather Service, and State of California, Dept. of Water Resources (1973). 32. Gupta, V.K. and Sorooshian, S. The automatic calibration of conceptual catchment models using derivative-based optimization algorithms", Water Resources Research, 21(4), pp. 473-485 (1985). 33. Ozelkan, E.C. and Duckstein, L. Fuzzy conceptual rainfall-runo_ models", Journal of Hydrology, 253(1- 4), pp. 41-68 (2001). 34. Hsu, K.L., Gupta, H.V., and Sorooshian, S. Arti- _cial neural network modeling of the rainfall-runo_ process", Water Resources Research, 31(10), pp. 2517-2530 (1995). 35. Cooper, V., Nguyen, V., and Nicell, J. Evaluation of global optimization methods for conceptual rainfallruno _ model calibration", Water Science and Technology, 36(5), pp. 53-60 (1997). 36. Duan, Q. Global optimization for watershed model calibration", Calibration of Watershed Models, 6, pp. 89-104 (2003). 37. Bergstrom, S., Development and Application of a Conceptual Runo_ Model for Scandinavian Catchments, 52, Department of Water Resources Engineering, Lund Institute of Technology, University of Lund (1976). 38. Boyle, D.P., Gupta, H.V., and Sorooshian, S. Multicriteria calibration of hydrologic models", Calibration of Watershed Models, Duan, Q., Gupta, H., Sorooshian, S., Rousseau, A., Turcotte, and R., AGU, Eds., pp. 185-196, Wiley Online Library 2003. M. Rahnamay Naeini et al./Scientia Iranica, Transactions A: Civil Engineering 26 (2019) 2015{2031 2025 39. Chiew, F.H., Peel, M.C., Western, A.W., et al. Application and testing of the simple rainfall-runo_ model SIMHYD", Mathematical Models of Small Watershed Hydrology and Applications, pp. 335-367 (2002). 40. Zhao, R.-J. The Xinanjiang model", In Proceedings of the Oxford Symposium (1980). 41. Leavesley, G., Lichty, R., Troutman, B., et al. Precipitation-runo_ modeling system: User's manual", Water-Resources Investigations Report, 83, p. 4238 (1983). 42. Thorstensen, A., Nguyen, P., Hsu, K., et al. Using densely distributed soil moisture observations for calibration of a hydrologic model", Journal of Hydrometeorology, 17(2), pp. 571-590 (2016). 43. Wang, J., Hong, Y., Li, L., et al. The coupled routing and excess storage (CREST) distributed hydrological model", Hydrological Sciences Journal, 56(1), pp. 84- 98 (2011). 44. Arnold, J.G., Srinivasan, R., Muttiah, R.S., et al. Large area hydrologic modeling and assessment part I: Model development", JAWRA Journal of the American Water Resources Association, 34(1), pp. 73-89 (1998). 45. Wood, E.F., Lettenmaier, D.P., and Zartarian, V.G. A land-surface hydrology parameterization with subgrid variability for general circulation models", Journal of Geophysical Research: Atmospheres, 97(D3), pp. 2717-2728 (1992). 46. Van Griensven, A. and Meixner, T. A global and e_cient multi-objective autocalibration and uncertainty estimation method for water quality catchment models", Journal of Hydroinformatics, 9(4), pp. 277- 291 (2007). 47. Yang, J., Reichert, P., Abbaspour, K.C., et al. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe basin in China", Journal of Hydrology, 358(1-2), pp. 1-23 (2008). 48. Abbaspour, K.C., SWAT-CUP 2012. SWAT Calibration and Uncertainty Program - A User Manual (2013). 49. Hogue, T.S., Gupta, H.V., Sorooshian, S., et al. A multi-step automatic calibration scheme for watershed models", Calibration of Watershed Models, 6, pp. 165-174 (2003). 50. Rozos, E. and Koutsoyiannis, D. A multicell karstic aquifer model with alternative ow equations", Journal of Hydrology, 325(1-4), pp. 340-355 (2006). 51. Van Griensven, A. and Bauwens, W. Multiobjective autocalibration for semidistributed water quality models", Water Resources Research, 39(12), 1348 (2003). 52. Alvisi, S., Franchini, M., and Marinelli, A. A stochastic model for representing drinking water demand at residential level" Water Resources Management, 17(3), pp. 197-222 (2003). 53. Franchini, M., Lamberti, P., and Di Giammarco, P. Rating curve estimation using local stages, upstream discharge data and a simpli_ed hydraulic model", Hydrology and Earth System Sciences Discussions, 3(4), pp. 541-548 (1999). 54. Winchell, M., Gupta, H.V., and Sorooshian, S. On the simulation of in_ltration- and saturation-excess runo_ using radar-based rainfall estimates: E_ects of algorithm uncertainty and pixel aggregation", Water Resources Research, 34(10), pp. 2655-2670 (1998). 55. Burton, A., Kilsby, C.G., Fowler, H., et al. Rainsim: A spatial-temporal stochastic rainfall modelling system", Environmental Modelling & Software, 23(12), pp. 1356-1369 (2008). 56. Ketabchi, H. and Ataie-Ashtiani, B. Evolutionary algorithms for the optimal management of coastal groundwater: a comparative study toward future challenges", Journal of Hydrology, 520, pp. 193-213 (2015). 57. Liong, S.-Y. and Atiquzzaman, M. Optimal design of water distribution network using shu_ed complex evolution", Journal of The Institution of Engineers, Singapore, 44(1), pp. 93-107 (2004). 58. Le Ngo, L., Madsen, H., and Rosbjerg, D. Simulation and optimisation modelling approach for operation of the Hoa Binh reservoir, Vietnam", Journal of Hydrology, 336(3-4), pp. 269-281 (2007). 59. She_eld, J. and Wood, E.F. Characteristics of global and regional drought, 1950-2000: Analysis of soil moisture data from o_-line simulation of the terrestrial hydrologic cycle", Journal of Geophysical Research: Atmospheres, 112, D17115 (2007). 60. Sadegh, M., Ragno, E., and AghaKouchak, A. Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework", Water Resources Research, 53(6), pp. 5166-5183 (2017). 61. Men_endez, M., M_endez, F.J., Izaguirre, C., et al. The inuence of seasonality on estimating return values of signi_cant wave height", Coastal Engineering, 56(3), pp. 211-219 (2009). 62. Menberu, M.W., Haghighi, A.T., Ronkanen, A.-K. et al. E_ects of drainage and subsequent restoration on peatland hydrological processes at catchment scale", Water Resources Research, 54(7), pp. 4479-4497 (2018). 63. Fang, W., Huang, S., Ren, K., et al. Examining the applicability of di_erent sampling techniques in the development of decomposition-based streamow forecasting models", Journal of Hydrology, 568, pp. 534-550 (2019). 64. Wang, W.-C. Chau, K.-W., Cheng, C.-T., et al. A comparison of performance of several arti_cial intelligence methods for forecasting monthly discharge time series", Journal of hydrology, 374(3-4), pp. 294-306 (2009). 2026 M. Rahnamay Naeini et al./Scientia Iranica, Transactions A: Civil Engineering 26 (2019) 2015{2031 65. Fang, W., Huang, S., Huang, Q., et al. Reference evapotranspiration forecasting based on local meteorological and global climate information screened by partial mutual information", Journal of Hydrology, 561, pp. 764-779 (2018). 66. Alvisi, S. and Franchini, M. Fuzzy neural networks for water level and discharge forecasting with uncertainty", Environmental Modelling & Software, 26(4), pp. 523-537 (2011). 67. Yang, T., Asanjan, A.A., Faridzad, M., et al. An enhanced arti_cial neural network with a shu_ed complex evolutionary global optimization with principal component analysis", Information Sciences, 418, pp. 302-316 (2017). 68. Gopalakrishnan, K. and Kim, S. Global optimization of pavement structural parameters during backcalculation using hybrid shu_ed complex evolution algorithm", Journal of Computing in Civil Engineering, 24(5), pp. 441-451 (2010). 69. Jiang, Y., Zhang, H., and Hongshan, X. Optimization strategy for air tra_c ow in multi-airport network", Scienti_c Research and Essays, 6(31), pp. 6499-6508 (2011). 70. Barakat, S.A. and Altoubat, S. Application of evolutionary global optimization techniques in the design of RC water tanks", Engineering Structures, 31(2), pp. 332-344 (2009). 71. Ma, G., Huang, J., Wu, W., et al. Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield", Mathematical and Computer Modelling, 58(3-4), pp. 634-643 (2013). 72. Bertaglia, G., Ioriatti, M., Valiani, A., et al. Numerical methods for hydraulic transients in visco-elastic pipes", Journal of Fluids and Structures, 81, pp. 230- 254 (2018). 73. Abascal, A.J., Castanedo, S., Mendez, F.J., et al. Calibration of a Lagrangian transport model using drifting buoys deployed during the prestige oil spill", Journal of Coastal Research, 25(1), pp. 80-90 (2009). 74. Edla, D.R., Lipare, A., and Cheruku, R. Shu_ed complex evolution approach for load balancing of gateways in wireless sensor networks", Wireless Personal Communications, 98(4), pp. 3455-3476 (2018). 75. Sharma, H.N., Kroonblawd, M.P., Sun, Y., et al. Role of _ller and its heterostructure on moisture sorption mechanisms in polyimide _lms", Scienti_c Reports, 8(1), p. 16889 (2018). 76. Schmidt, F., Wagner, N., Mai, J., et al. Dielectric spectra reconstruction of layered multiphase soil", In 2018 12th International Conference on Electromagnetic Wave Interaction with Water and Moist Substances (ISEMA), IEEE, pp. 1-9 (2018). 77. Lazare_, B., Berger, J.P., Kluska, J., et al. Structure of herbig aebe disks at the milliarcsecond scale-a statistical survey in the h band using pionier-vlti", Astronomy & Astrophysics, 599, p. A85 (2017). 78. Shahriari R. and Dehghani, M.R. New electrolyte SAFT-VR Morse EOS for prediction of solid-liquid equilibrium in aqueous electrolyte solutions", Fluid Phase Equilibria, 463, pp. 128-141 (2018). 79. Yang, T., Hsu, K., Duan, Q., et al. Method to estimate optimal parameters", Handbook of Hydrometeorological Ensemble Forecasting, pp. 1-39, Springer Berlin Heidelberg (2018). 80. Wu, B., Zheng, Y., Wu, X., et al. Optimizing water resources management in large river basins with integrated surface water-groundwater modeling: A surrogate-based approach", Water Resources Research, 51(4), pp. 2153-2173 (2015). 81. Ketabchi, H. and Ataie-Ashtiani, B. Coastal groundwater optimization-advances, challenges, and practical", Hydrogeology Journal, 23(6), pp. 1129-1154 (2015). 82. Gan, Y., Liang, X.-Z., Duan, Q., et al. A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model", Journal of Hydrology, 564, pp. 697-711 (2018). 83. Wang, C., Duan, Q., Gong, W., et al. An evaluation of adaptive surrogate modeling based optimization with two benchmark problems", Environmental Modelling & Software, 60, pp. 167-179 (2014). 84. Gupta, H.V., Sorooshian, S., and Yapo, P.O. Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information", Water Resources Research, 34(4), pp. 751-763 (1998). 85. Boyle, D.P., Gupta, H.V., and Sorooshian, S. Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods", Water Resources Research, 36(12), pp. 3663-3674 (2000). 86. Gupta, H.V., Bastidas, L., Sorooshian, S., et al. Parameter estimation of a land surface scheme using multicriteria methods", Journal of Geophysical Research: Atmospheres, 104(D16), pp. 19491-19503 (1999). 87. Anghileri, D., Voisin, N., Castelletti, A., et al. Value of long-term streamow forecasts to reservoir operations for water supply in snow-dominated river catchments", Water Resources Research, 52(6), pp. 4209-4225 (2016). 88. Meixner, T., Bastidas, L., Gupta, H.V., et al. Multicriteria parameter estimation for models of stream chemical composition", Water Resources Research, 38(3), pp. 9-1 (2002). 89. Engeland, K., Braud, I., Gottschalk, L., et al. Multiobjective regional modelling", Journal of Hydrology, 327(3-4), pp. 339-351 (2006). 90. Collischonn, W., Haas, R., Andreolli, I., et al. Forecasting river Uruguay ow using rainfall forecasts from a regional weather-prediction model", Journal of Hydrology, 305(1-4), pp. 87-98 (2005). 91. Beldring, S. Multi-criteria validation of a precipitation-runo_ model", Journal of Hydrology, 257(1-4), pp. 189-211 (2002). M. Rahnamay Naeini et al./Scientia Iranica, Transactions A: Civil Engineering 26 (2019) 2015{2031 2027 92. Magnani, F., Mencuccini, M., Borghetti, M., et al. The human footprint in the carbon cycle of temperate and boreal forests", Nature, 447(7146), p. 849 (2007). 93. Naseem, B., Ajami, H., Liu, Y., et al. Multiobjective assessment of three remote sensing vegetation products for streamow prediction in a conceptual ecohydrological model", Journal of Hydrology, 543, pp. 686-705 (2016). 94. Leavesley, G., Markstrom, S., Restrepo, P.J., et al. A modular approach to addressing model design, scale, and parameter estimation issues in distributed hydrological modelling", Hydrological Processes, 16(2), pp. 173-187 (2002). 95. Ajami, N.K., Duan, Q., and Sorooshian, S. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction", Water Resources Research, 43(1), W01403 (2007). 96. Moradkhani, H., Sorooshian, S., Gupta, H.V., et al. Dual state-parameter estimation of hydrological models using ensemble Kalman _lter", Advances in Water Resources, 28(2), pp. 135-147 (2005). 97. Vrugt, J.A., Diks, C.G., Gupta, H.V., et al. Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation", Water Resources Research, 41(1), W01017 (2005). 98. Schoups, G., Hopmans, J.W., Young, C., et al. Multi-criteria optimization of a regional spatiallydistributed subsurface water ow model", Journal of Hydrology, 311(1-4), pp. 20-48 (2005). 99. Feyen, L., Vrugt, J.A., _ O. Nuall_ain, B., et al. Parameter optimisation and uncertainty assessment for large-scale streamow simulation with the lisood model", Journal of Hydrology, 332(3-4), pp. 276-289 (2007). 100. Parajka, J., Merz, R., and Bloschl, G. Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments", Hydrological Processes: An International Journal, 21(4), pp. 435-446 (2007). 101. Jiang, S., Ren, L., Hong, Y., et al. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological ows using the Bayesian model averaging method", Journal of Hydrology, 452, pp. 213-225 (2012). 102. McMillan, H., Freer, J., Pappenberger, F., et al. Impacts of uncertain river ow data on rainfallruno _ model calibration and discharge predictions", Hydrological Processes: An International Journal, 24(10), pp. 1270-1284 (2010). 103. Fenicia, F., Savenije, H.H., Matgen, P., et al. Understanding catchment behavior through stepwise model concept improvement", Water Resources Research, 44(1), W01402 (2008). 104. Blasone, R.-S., Vrugt, J.A., Madsen, H., et al. Generalized likelihood uncertainty estimation (glue) using adaptive Markov chain Monte Carlo sampling", Advances in Water Resources, 31(4), pp. 630-648 (2008). 105. Beven, K. and Binley, A. The future of distributed models: model calibration and uncertainty prediction", Hydrological Processes, 6(3), pp. 279-298 (1992). 106. Blasone, R.-S., Madsen, H., and Rosbjerg, D. Uncertainty assessment of integrated distributed hydrological models using glue with Markov chain Monte Carlo sampling", Journal of Hydrology, 353(1-2), pp. 18-32 (2008). 107. Haddeland, I., Skaugen, T., and Lettenmaier, D.P. Anthropogenic impacts on continental surface water uxes", Geophysical Research Letters, 33(8), L08406 (2006). 108. Kan, G., He, X., Li, J., et al. Computer aided numerical methods for hydrological model calibration: An overview and recent development", Archives of Computational Methods in Engineering, 26(1), pp. 35-59 (2019). 109. Xue, X., Hong, Y., Limaye, A.S., et al. Statistical and hydrological evaluation of TRMM based multisatellite precipitation analysis over the Wangchu basin of Bhutan: Are the latest satellite precipitation products 3B42V7 ready for use in ungauged basins", Journal of Hydrology, 499, pp. 91-99 (2013). 110. Shen X. and Anagnostou, E.N. A framework to improve hyper-resolution hydrological simulation in snow-a_ected regions", Journal of Hydrology, 552, pp. 1-12 (2017). 111. Gao, Z., Long, D., Tang, G., et al. Assessing the potential of satellite-based precipitation estimates for ood frequency analysis in ungauged or poorly gauged tributaries of China's Yangtze river basin", Journal of Hydrology, 550, pp. 478-496 (2017). 112. Ma, Q., Xiong, L., Liu, D., et al. Evaluating the temporal dynamics of uncertainty contribution from satellite precipitation input in rainfall-runo_ modeling using the variance decomposition method", Remote Sensing, 10(12), p. 1876 (2018). 113. Sun, W., Ma, J., Yang, G., et al. Statistical and hydrological evaluations of multi-satellite precipitation products over Fujiang river basin in humid southeast China", Remote Sensing, 10(12), p. 1898 (2018). 114. Yu, P.-S., Yang, T.-C., and Wu, C.-K. Impact of climate change on water resources in southern Taiwan", Journal of Hydrology, 260(1-4), pp. 161-175 (2002). 115. Parajka, J., Merz, R., and Bloschl, G. A comparison of regionalisation methods for catchment model parameters", Hydrology and Earth System Sciences Discussions, 9(3), pp. 157-171 (2005). 2028 M. Rahnamay Naeini et al./Scientia Iranica, Transactions A: Civil Engineering 26 (2019) 2015{2031 116. Parajka, J. and Bloschl, G. The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models", Journal of Hydrology, 358(3-4), pp. 240-258 (2008). 117. Merz, R., Parajka, J., and Bloschl, G. Time stability of catchment model parameters: Implications for climate impact analyses", Water Resources Research, 47(2), W02531 (2011). 118. Dakhlaoui, H., Bargaoui, Z., and B_ardossy, A. Toward a more e_cient calibration schema for HBV rainfall-runo_ model", Journal of Hydrology, 444, pp. 161-179 (2012). 119. Duan, Q., Ajami, N.K., Gao, X., et al. Multi-model ensemble hydrologic prediction using Bayesian model averaging", Advances in Water Resources, 30(5), pp. 1371-1386 (2007). 120. Naja_, M., Moradkhani, H., and Jung, I. Assessing the uncertainties of hydrologic model selection in climate change impact studies", Hydrological Processes, 25(18), pp. 2814-2826 (2011). 121. Naseem, B., Ajami, H., Cordery, I., et al. A multiobjective assessment of alternate conceptual ecohydrological models", Journal of Hydrology, 529, pp. 1221-1234 (2015). 122. Abbott, M., Bathurst, J., Cunge, J., et al. An introduction to the European hydrological system { systeme hydrologique Europeen,SHE", 2: Structure of a physically-based, distributed modelling system", Journal of hydrology, 87(1-2), pp. 61-77 (1986). 123. Mertens, J., Madsen, H., Feyen, L., et al. Including prior information in the estimation of e_ective soil parameters in unsaturated zone modelling", Journal of Hydrology, 294(4), pp. 251-269 (2004). 124. Stisen, S. and Sandholt, I. Evaluation of remotesensing- based rainfall products through predictive capability in hydrological runo_ modelling", Hydrological Processes: An International Journal, 24(7), pp. 879-891 (2010). 125. Liu, J., Liu, T., Bao, A., et al. Response of hydrological processes to input data in high alpine catchment: An assessment of the Yarkant river basin in China", Water, 8(5), p. 181 (2016). 126. Li, D., Liang, Z., Li, B., et al. Multi-objective calibration of MIKE SHE with SMAP soil moisture datasets", Hydrology Research, 50(2), pp. 644-654 (2018). 127. Thomas. H. Improved methods for national water assessment", Report WR15249270, US Water Resource Council, Washington, DC (1981). 128. Sankarasubramanian, A., Vogel, R.M., and Limbrunner, J.F. Climate elasticity of streamow in the united states", Water Resources Research, 37(6), pp. 1771-1781 (2001). 129. Sankarasubramanian, A. and Vogel, R.M. Annual hydroclimatology of the united states", Water Resources Research, 38(6), pp. 19-1-19-12 (2002). 130. Vogel, R.M. and Sankarasubramanian, A. Validation of a watershed model without calibration", Water Resources Research, 39(10), 1292 (2003). 131. Martinez, G.F. and Gupta, H.V. Toward improved identi_cation of hydrological models: A diagnostic evaluation of the abcd" monthly water balance model for the conterminous united states", Water Resources Research, 46(8), W08507 (2010). 132. Deng, C., Liu, P., Wang, D., et al. Temporal variation and scaling of parameters for a monthly hydrologic model", Journal of Hydrology, 558, pp. 290-300 (2018). 133. Ajami, N.K., Gupta, H., Wagener, T., et al. Calibration of a semi-distributed hydrologic model for streamow estimation along a river system", Journal of Hydrology, 298(1-4), pp. 112-135 (2004). 134. Behrangi, A., Khakbaz, B., Jaw, T.C., et al. Hydrologic evaluation of satellite precipitation products over a mid-size basin", Journal of Hydrology, 397(3- 4), pp. 225-237 (2011). 135. Gan, T.Y. and Biftu, G.F. Automatic calibration of conceptual rainfall-runo_ models: Optimization algorithms, catchment conditions, and model structure", Water Resources Research, 32(12), pp. 3513- 3524 (1996). 136. Gusev Y.M. and Nasonova, O. The land surface parameterization scheme swap: Description and partial validation", Global and Planetary Change, 19(1-4), pp. 63-86 (1998). 137. Nasonova, O.N., Gusev, Y.M., and Kovalev, Y.E. Investigating the ability of a land surface model to simulate streamow with the accuracy of hydrological models: A case study using MOPEX materials", Journal of Hydrometeorology, 10(5), pp. 1128-1150 (2009). 138. Gusev, E., Nasonova, O.N., and Dzhogan, L.Y. Physically based modeling of many-year dynamics of daily streamow and snow water equivalent in the lena R. basin", Water Resources, 43(1), pp. 21-32 (2016). 139. Nasonova, O.N., Gusev, Y.M., Volodin, E.M., et al. Application of the land surface model SWAP and global climate model INMCM4.0 for projecting runo_ of northern Russian rivers. 1. Historical simulations", Water Resources, 45(2), pp. 73-84 (2018). 140. Gusev, E., Nasonova, O.N., Kovalev, E., et al. Modelling water balance components of river basins located in di_erent regions of the globe", Water Resources, 45(2), pp. 53-64 (2018). 141. Eckhardt, K. and Arnold, J. Automatic calibration of a distributed catchment model", Journal of Hydrology, 251(1-2), pp. 103-109 (2001). 142. Van Liew, M.W., Veith, T.L., Bosch, D.D., et al. Suitability of SWAT for the conservation e_ects assessment project: Comparison on USDA agricultural research service watersheds", Journal of Hydrologic Engineering, 12(2), pp. 173-189 (2007). M. Rahnamay Naeini et al./Scientia Iranica, Transactions A: Civil Engineering 26 (2019) 2015{2031 2029 143. Green, C. and Van Griensven, A. Autocalibration in hydrologic modeling: Using SWAT2005 in small-scale watersheds", Environmental Modelling & Software, 23(4), pp. 422-434 (2008). 144. Yu, D., Xie, P., Dong, X., et al. Improvement of the SWAT model for event-based ood simulation on a sub-daily timescale", Hydrology and Earth System Sciences, 22(9), pp. 5001-5019 (2018). 145. Rouhani, H. and Leconte, R. A methodological framework to assess PMP and PMF in snowdominated watersheds under changing climate conditions - A case study of three watersheds in Qu_ebec (Canada)", Journal of Hydrology, 561, pp. 796-809 (2018). 146. Chiew, F., Kirono, D., Kent, D., et al. Comparison of runo_ modelled using rainfall from di_erent downscaling methods for historical and future climates", Journal of Hydrology, 387(1-2), pp. 10-23 (2010). 147. Vaze, J., Post, D., Chiew, F., et al. Conceptual rainfall-runo_ model performance with di_erent spatial rainfall inputs", Journal of Hydrometeorology, 12(5), pp. 1100-1112 (2011). 148. Duan, D. and Mei, Y. Comparison of meteorological, hydrological and agricultural drought responses to climate change and uncertainty assessment", Water Resources Management, 28(14), pp. 5039-5054 (2014). 149. Khan, U., Ajami, H., Tuteja, N.K., et al. Catchment scale simulations of soil moisture dynamics using an equivalent cross-section based hydrological modelling approach", Journal of Hydrology, 564, pp. 944-966 (2018). 150. Potter, N., Ekstrom, M., Chiew, F., et al. Changesignal impacts in downscaled data and its inuence on hydroclimate projections", Journal of Hydrology, 564, pp. 12-25 (2018). 151. Rossman, L.A. Storm water management model user's manual", version 5.0. Cincinnati: National Risk Management Research Laboratory, O_ce of Research and Development, US Environmental Protection Agency (2010). 152. Lee, S. and Kang, T. Analysis of constrained optimization problems by the SCE-UA with an adaptive penalty function", Journal of Computing in Civil Engineering, 30(3), p. 04015035 (2015). 153. Russwurm, I.L., Johannessen, B.G., Gragne, A.S., et al. Modelling green roof detention performance in cold climates", EPiC Series in Engineering, Easy- Chair, 3, pp. 1804-1813 (2018). 154. Hamouz, V. and Muthanna, T.M. Modelling of green and grey roofs in cold climates using EPA's storm water management model", In International Conference on Urban Drainage Modelling, pp. 385- 391, Springer (2018). 155. Johannessen, B.G., Hamouz, V., Gragne, A.S., et al. The transferability of SWMM model parameters between green roofs with similar build-up", Journal of Hydrology, 569, pp. 816-828 (2019). 156. Beven, K.J. and Kirkby, M.J. A physically based, variable contributing area model of basin hydrology/ un mod_ele _a base physique de zone d'appel variable de l'hydrologie du bassin versant", Hydrological Sciences Journal, 24(1), pp. 43-69 (1979). 157. Kavetski, D., Franks, S.W., and Kuczera, G. Confronting input uncertainty in environmental modelling", Calibration of Watershed Models, 6, pp. 49-68 (2003). 158. Hossain, F., Anagnostou, E.N., and Bagtzoglou, A.C. On latin hypercube sampling for e_cient uncertainty estimation of satellite rainfall observations in ood prediction", Computers & Geosciences, 32(6), pp. 776-792 (2006). 159. Qi, W., Zhang, C., Fu, G., et al. Quantifying dynamic sensitivity of optimization algorithm parameters to improve hydrological model calibration", Journal of Hydrology, 533, pp. 213-223 (2016). 160. Rogelis, M.C. and Werner, M. Streamow forecasts from WRF precipitation for ood early warning in mountain tropical areas", Hydrology and Earth System Sciences, 22(1), pp. 853-870 (2018). 161. Hay, L., Clark, M., Pagowski, M., et al. One-way coupling of an atmospheric and a hydrologic model in Colorado", Journal of Hydrometeorology, 7(4), pp. 569-589 (2006). 162. Viger, R.J., Hay, L.E., Markstrom, S.L., et al. Hydrologic e_ects of urbanization and climate change on the int river basin, Georgia", Earth Interactions, 15(20), pp. 1-25 (2011). 163. Ott, I., Duethmann, D., Liebert, J., et al. Highresolution climate change impact analysis on mediumsized river catchments in Germany: an ensemble assessment", Journal of Hydrometeorology, 14(4), pp. 1175-1193 (2013). 164. Mendoza, P.A., Clark, M.P., Mizukami, N., et al. E_ects of hydrologic model choice and calibration on the portrayal of climate change impacts", Journal of Hydrometeorology, 16(2), pp. 762-780 (2015). 165. Abdulla, F.A., Lettenmaier, D.P., Wood, E.F., et al. Application of a macroscale hydrologic model to estimate the water balance of the Arkansas-Red river basin", Journal of Geophysical Research: Atmospheres, 101(D3), pp. 7449-7459 (1996). 166. Wooldridge, S., Kalma, J.D., and Walker, J.P. Importance of soil moisture measurements for inferring parameters in hydrologic models of low-yielding ephemeral catchments", Environmental Modelling & Software, 18(1), pp. 35-48 (2003). 167. Troy, T.J., Wood, E.F., and She_eld, J. An e_cient calibration method for continental-scale land surface modeling", Water Resources Research, 44(9) (2008). 168. She_eld, J., Wood, E.F., Chaney, N., et al. A drought monitoring and forecasting system for Sub- Sahara African water resources and food security", Bulletin of the American Meteorological Society, 95(6), pp. 861-882 (2014). 2030 M. Rahnamay Naeini et al./Scientia Iranica, Transactions A: Civil Engineering 26 (2019) 2015{2031 169. Mizukami, N., Clark, M.P., Newman, A.J., et al. Towards seamless large-domain parameter estimation for hydrologic models", Water Resources Research, 53(9), pp. 8020-8040 (2017). 170. Gan, T.Y., Dlamini, E.M., and Biftu, G.F. E_ects of model complexity and structure, data quality, and objective functions on hydrologic modeling", Journal of Hydrology, 192(1-4), pp. 81-103 (1997). 171. Guo, W., Wang, C., Zeng, X., et al. Subgrid parameterization of the soil moisture storage capacity for a distributed rainfall-runo_ model", Water, 7(6), pp. 2691-2706 (2015). 172. Yuan, F., Zhang, L., Win, K., et al. Assessment of GPM and TRMM multi-satellite precipitation products in streamow simulations in a data-sparse mountainous watershed in Myanmar", Remote Sensing, 9(3), p. 302 (2017). 173. Zeng, Q., Chen, H., Xu, C.-Y., et al. The e_ect of rain gauge density and distribution on runo_ simulation using a lumped hydrological modelling approach", Journalof Hydrology, 563, pp. 106-122 (2018).
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