• Arnold, J. G., J. R. Williams, R. Srinivasan, and K. W. King, 1996: The Soil and Water Assessment Tool (SWAT) user’s manual. Grassland, Soil and Water Research Laboratory, Agriculture Research Service, USDA, 102 pp.

  • Bai, S., M. Li, R. Kong, S. Han, H. Li, and L. Qin, 2019: Data mining approach to construction productivity prediction for cutter suction dredgers. Autom. Constr., 105, 102833, https://doi.org/10.1016/j.autcon.2019.102833.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balkhair, K. S., and Coauthors, 2018: Groundwater share quantification through flood hydrographs simulation using two temporal rainfall distributions. Desalin. Water Treat., 114, 109119, https://doi.org/10.5004/dwt.2018.22346.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baltas, E. A., N. A. Dervos, and M. A. Mimikou, 2007: Determination of the SCS initial abstraction ratio in an experimental watershed in Greece. Hydrol. Earth Syst. Sci., 11, 18251829, https://doi.org/10.5194/hess-11-1825-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bathurst, J. C., and Coauthors, 2011: Forest impact on floods due to extreme rainfall and snowmelt in four Latin American environments 1: Field data analysis. J. Hydrol., 400, 281291, https://doi.org/10.1016/j.jhydrol.2010.11.044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bathurst, J. C., S. J. Birkinshaw, F. Cisneros Espinosa, and A. Iroumé, 2016: Forest impact on flood peak discharge and sediment yield in streamflow. River Syst. Anal. Manage., 1529, https://doi.org/10.1007/978-981-10-1472-7_2.

    • Search Google Scholar
    • Export Citation
  • Beaudoing, H. K., M. Rodell, and NASA/GSFC/HSL, 2015: GLDAS Noah land surface model L4 3 hourly 0.25 × 0.25 degree V2.0. Goddard Earth Sciences Data and Information Services Center (GES DISC), accessed 24 April 2019, https://doi.org/10.5067/342OHQM9AK6Q.

    • Crossref
    • Export Citation
  • Bhuiyan, H. A. K. M., H. McNairn, J. Powers, and A. Merzouki, 2017: Application of HEC-HMS in a cold region watershed and use of RADARSAT-2 soil moisture in initializing the model. Hydrology, 4, 9, https://doi.org/10.3390/hydrology4010009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Birkinshaw, S. J., J. C. Bathurst, A. Iroumé, and H. Palacios, 2010: The effect of forest cover on peak flow and sediment discharge—An integrated field and modelling study in central–southern Chile. Hydrol. Processes, 25, 12841297, https://doi.org/10.1002/hyp.7900.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Box, G. E. P., and G. M. Jenkins, 1970: Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, 537 pp.

  • Brocca, L., F. Melone, T. Moramarco, and V. P. Singh, 2009: Assimilation of observed soil moisture data in storm rainfall-runoff modeling. J. Hydrol. Eng., 14, 153165, https://doi.org/10.1061/(ASCE)1084-0699(2009)14:2(153).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chollet, F., 2016: Keras documentation: The Python deep learning library. Accessed 18 April 2018, https://keras.io.

  • Dunkerley, D., 2008: Identifying individual rain events from pluviograph records: A review with analysis of data from an Australian dryland site. Hydrol. Processes, 22, 50245036, https://doi.org/10.1002/hyp.7122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunne, T., 1978: Field studies of hillslope flow processes. Hillslope Hydrology, M. J. Kirkby, Ed., Wiley, 227–293.

  • Fischer, S., A. Schumann, and M. Schulte, 2016: Characterisation of seasonal flood types according to timescales in mixed probability distributions. J. Hydrol., 539, 3856, https://doi.org/10.1016/j.jhydrol.2016.05.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, S., G. Zhang, N. Wang, and L. Luo, 2011: Initial abstraction ratio in the SCS-CN method in the Loess Plateau of China. Trans. ASABE, 54, 163169, https://doi.org/10.13031/2013.36271.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Furey, P. R., and V. K. Gupta, 2005: Effects of excess rainfall on the temporal variability of observed peak-discharge power laws. Adv. Water Resour., 28, 12401253, https://doi.org/10.1016/j.advwatres.2005.03.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Green, W. H., and G. A. Ampt, 1911: Studies on soil physics, Part I. The flow of air and water through soils. J. Agric. Sci., 4, 1124.

    • Search Google Scholar
    • Export Citation
  • Gupta, V. K., I. Rodríguez-Iturbe, and E. F. Wood, 1986: Scale Problems in Hydrology Runoff Generation and Basin Response. Springer, 246 pp.

  • Hawkins, R. H., A. T. Hjelmfelt Jr., and A. W. Zevenbergen, 1985: Runoff probability, storm depth, and curve numbers. J. Irrig. Drain. Eng., 111, 330340, https://doi.org/10.1061/(ASCE)0733-9437(1985)111:4(330).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, R. H., R. Jiang, D. E. Woodward, A. T. Hjelmfelt, J. A. Van Mullem, and Q. D. Quan, 2002: Runoff curve number method: Examination of the initial abstraction ratio. Second Federal Interagency Hydrologic Modeling Conf., Las Vegas, NV, Water Information Coordination Program, 16 pp.

  • Hawkins, R. H., T. J. Ward, D. E. Woodward, and J. A. Van Mullen, 2009: Curve Number Hydrology-State of the Practice. American Society of Civil Engineers, 106 pp., https://doi.org/10.1061/9780784410042.

    • Search Google Scholar
    • Export Citation
  • Hawkins, R. H., F. D. Theurer, and M. Rezaeianzadeh, 2019: Understanding the basis of the curve number method for watershed models and TMDLs. J. Hydrol. Eng., 24, 06019003, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001755.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horton, R. E., 1933: The role of infiltration in the hydrologic cycle. Eos, Trans. Amer. Geophys. Union, 14, 446460, https://doi.org/10.1029/TR014i001p00446.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horton, R. E., 1941: An approach toward a physical interpretation of infiltration-capacity. Soil Sci. Soc. Amer. Proc., 4, 399417, https://doi.org/10.2136/SSSAJ1941.036159950005000C0075X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, C., Q. Wu, H. Li, S. Jian, N. Li, and Z. Lou, 2018: Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water, 10, 1543, https://doi.org/10.3390/w10111543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kashid, S. S., and R. Maity, 2012: Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming. J. Hydrol., 454–455, 2641, https://doi.org/10.1016/j.jhydrol.2012.05.033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaur, H., and V. Jothiprakash, 2013: Daily precipitation mapping and forecasting using data driven techniques. Int. J. Hydrol. Sci. Technol., 3, 364377, https://doi.org/10.1504/IJHST.2013.060337.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knisel, W. G., Ed., 1980: CREAMS: A Field-Scale Model for Chemical, Runoff and Erosion from Agricultural Management Systems. Conservation Research Report, Vol. 26, Department of Agriculture, Science & Education Administration, 643 pp.

  • Koneti, S., S. L. Sunkara, and P. S. Roy, 2018: Hydrological modeling with respect to impact of land-use and land-cover change on the runoff dynamics in Godavari river basin using the HEC-HMS model. Int. J. Geo-Inf., 7, 206, https://doi.org/10.3390/IJGI7060206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, D., A. Singh, P. Samui, and R. K. Jha, 2019: Forecasting monthly precipitation using sequential modelling. Hydrol. Sci. J., 64, 690700, https://doi.org/10.1080/02626667.2019.1595624.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., M. Liu, Y. Hu, J. Gong, and Y. Xu, 2016: Modeling the quality and quantity of runoff in a highly urbanized catchment using storm water management model. Pol. J. Environ. Stud., 25, 15731581, https://doi.org/10.15244/pjoes/60721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., F. Li, H. Li, C. Guo, and W. Dong, 2019: Analysis of rainfall infiltration and its influence on groundwater in rain gardens. Environ. Sci. Pollut. Res. Int., 26, 22 64122 655, https://doi.org/10.1007/s11356-019-05622-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J.-Z., and P. Feng, 2011: The effects of underlying surface change on floods in Zijingguan watershed. Geogr. Res., 30, 921930, https://doi.org/10.11821/YJ2011050016.

    • Search Google Scholar
    • Export Citation
  • Lin, M., X. Chen, Y. Chen, and H. Yao, 2013: Improving calibration of two key parameters in Hydrologic Engineering Center hydrologic modelling system, and analysing the influence of initial loss on flood peak flows. Water Sci. Technol., 68, 27182724, https://doi.org/10.2166/wst.2013.562.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ling, H., C. Qian, W. Kang, C. Liang, and H. Chen, 2019: Combination of support vector machine and K-fold cross validation to predict compressive strength of concrete in marine environment. Constr. Build. Mater., 206, 355363, https://doi.org/10.1016/j.conbuildmat.2019.02.071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., Y. Liu, and W. Wang, 2019: Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis. Remote Sens. Environ., 220, 118, https://doi.org/10.1016/j.rse.2018.10.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loague, K., C. S. Heppner, B. A. Ebel, and J. E. VanderKwaak, 2010: The quixotic search for a comprehensive understanding of hydrologic response at the surface: Horton, Dunne, Dunton and the role of concept-development simulation. Hydrol. Processes, 24, 24992505, https://doi.org/10.1002/HYP.7834.

    • Search Google Scholar
    • Export Citation
  • Loukas, A., and M. C. Quick, 1996: Spatial and temporal distribution of storm precipitation in southwestern British Columbia. J. Hydrol., 174, 3756, https://doi.org/10.1016/0022-1694(95)02754-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manfroi, O. J., K. Koichiro, T. Nobuaki, S. Masakazu, M. Nakagawa, T. Nakashizuka, and L. Chong, 2004: The stemflow of trees in a Bornean lowland tropical forest. Hydrol. Processes, 18, 24552474, https://doi.org/10.1002/hyp.1474.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McDonough, K. R., S. L. Hutchinson, J. M. S. Hutchinson, J. L. Case, and V. Rahmani, 2018: Validation and assessment of SPoRT-LIS surface soil moisture estimates for water resources management applications. J. Hydrol., 566, 4354, https://doi.org/10.1016/j.jhydrol.2018.09.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mekanik, F., M. A. Imteaz, S. Gato-Trinidad, and A. Elmahdi, 2013: Multiple regression and artificial neural network for long-term rainfall forecasting using large scale climate modes. J. Hydrol., 503, 1121, https://doi.org/10.1016/j.jhydrol.2013.08.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ministry of Water Resources, 2006: Guidelines for Calculating Design Flood of Water Resources and Hydropower Projects. Chinese Water Resources and Hydropower Press, 80 pp.

  • Mishra, S. K., M. K. Jain, R. P. Pandey, and V. P. Singh, 2003: Evaluation of AMC-dependent SCS-CN-based models using large data of small watersheds. Water Energy Int., 60, 1323.

    • Search Google Scholar
    • Export Citation
  • Mishra, S. K., R. K. Sahu, T. I. Eldho, and M. K. Jain, 2006: An improved Ia-S relation incorporating antecedent moisture in SCS-CN methodology. Water Resour. Manage., 20, 643660, https://doi.org/10.1007/s11269-005-9000-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mishra, S. K., P. S. Babu, and V. P. Singh, 2007: SCS-CN method revisited. Advances in Hydraulics and Hydrology, V. P. Singh, Ed., Water Resources Publication, 36 pp.

  • Moustris, K. P., I. K. Larissi, P. T. Nastos, and A. G. Paliatsos, 2011: Precipitation forecast using artificial neural networks in specific regions of Greece. Water Resour. Manage., 25, 19791993, https://doi.org/10.1007/s11269-011-9790-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nathan, R. J., and T. A. McMahon, 1990: Evaluation of automated techniques for base flow and recession analyses. Water Resour. Res., 26, 14651473, https://doi.org/10.1029/WR026i007p01465.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noori, N., L. Kalin, P. Srivastava, and C. Lebleu, 2012: Effects of initial abstraction ratio in SCS-CN method on modeling the impacts of urbanization on peak flows. Proc. World Environmental and Water Resources Congress 2012, Albuquerque, NM, American Society of Civil Engineers, 329–338, http://doi.org/10.1061/9780784412312.036.

    • Crossref
    • Export Citation
  • Ouyang, Q., W. Lu, X. Xin, Y. Zhang, W. Cheng, and T. Yu, 2016: Monthly rainfall forecasting using EEMD-SVR based on phase-space reconstruction. Water Resour. Manage., 30, 23112325, https://doi.org/10.1007/s11269-016-1288-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rahman, A., P. E. Weinmann, T. M. T. Hoang, and E. M. Laurenson, 2002: Monte Carlo simulation of flood frequency curves from rainfall. J. Hydrol., 256, 196210, https://doi.org/10.1016/S0022-1694(01)00533-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramana, R. V., B. Krishna, S. R. Kumar, and N. G. Pandey, 2013: Monthly rainfall prediction using wavelet neural network analysis. Water Resour. Manage., 27, 36973711, https://doi.org/10.1007/s11269-013-0374-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reshef, D. N., and Coauthors, 2011: Detecting novel associations in large data sets. Science, 334, 15181524, https://doi.org/10.1126/science.1205438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381394, https://doi.org/10.1175/BAMS-85-3-381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saaty, T. L., 1977: A scaling method for priorities in hierarchical structures. J. Math. Psychol., 15, 234281, https://doi.org/10.1016/0022-2496(77)90033-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sahu, R. K., S. K. Mishra, T. I. Eldho, and M. K. Jain, 2007: An advanced soil moisture accounting procedure for SCS curve number method. Hydrol. Processes, 21, 28722881, https://doi.org/10.1002/hyp.6503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sanyal, J., A. L. Densmore, and P. Carbonneau, 2014: Analysing the effect of land use/cover changes at sub-catchment levels on downstream flood peaks: A semi-distributed modelling approach with sparse data. Catena, 118, 2840, https://doi.org/10.1016/j.catena.2014.01.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwab, M. P., J. Klaus, L. Pfister, and M. Weiler, 2017: How runoff components affect the export of DOC and nitrate: A long-term and high-frequency analysis. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-416.

    • Search Google Scholar
    • Export Citation
  • SCS, 1956: Chapter 10: Estimation of Direct Runoff from Storm Rainfall, Supplement A. National Engineering Handbook, Section 4: Hydrology, Soil Conservation Service, USDA, 1–28.

  • SCS, 1972: National Engineering Handbook, Section 4: Hydrology. Soil Conservation Service, U.S. Department of Agriculture, 28 pp.

  • Scussolini, P., J. C. J. H. Aerts, B. Jongman, L. M. Bouwer, H. C. Winsemius, H. De Moel, and P. J. Ward, 2016: FLOPROS: An evolving global database of flood protection standards. Nat. Hazards Earth Syst. Sci., 16, 10491061, https://doi.org/10.5194/nhess-16-1049-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shan, S., 2016: Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning. Integrated Series in Information System, Vol. 36, Springer, 359 pp.,https://doi.org/10.1007/978-1-4899-7641-3.

    • Crossref
    • Export Citation
  • Shi, Z. H., L. D. Chen, N. F. Fang, D. F. Qin, and C. F. Cai, 2009: Research on the SCS-CN initial abstraction ratio using rainfall–runoff event analysis in the Three Gorges Area, China. Catena, 77 (1), 17, https://doi.org/10.1016/j.catena.2008.11.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simanton, J. R., R. H. Hawkins, M. Mohseni-Saravi, and K. G. Renard, 1996: Runoff curve number variation with drainage area, Walnut Gulch, Arizona. Trans. ASAE, 39, 13911394, https://doi.org/10.13031/2013.27630.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, P. K., S. K. Mishra, R. Berndtsson, M. K. Jain, and R. P. Pandey, 2015: Development of a modified SMA based MSCS-CN model for runoff estimation. Water Resour. Manage., 29, 41114127, https://doi.org/10.1007/s11269-015-1048-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, W. G., and S. K. Carey, 2017: HydRun: A MATLAB toolbox for rainfall–runoff analysis. Hydrol. Processes, 31, 26702682, https://doi.org/10.1002/hyp.11185.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tarasova, L., S. Basso, M. Zink, and R. Merz, 2018: Exploring controls on rainfall-runoff events: 1. Time series-based event separation and temporal dynamics of event runoff response in Germany. Water Resour. Res., 54, 77117732, https://doi.org/10.1029/2018WR022587.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tayfur, G., and V. P. Singh, 2008: Closure to “ANN and fuzzy logic models for simulating event-based rainfall-runoff” J. Hydrol. Eng., 134, 14001401, https://doi.org/10.1061/(ASCE)0733-9429(2008)134:9(1400.2).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • USACE, 2008: HEC-Hydrologic modeling system (HEC-HMS)-application guide. U.S Army Corps of Engineers, 118 pp., https://www.hec.usace.army.mil/software/hec-hms/documentation/HEC-HMS_Applications_Guide_March2008.pdf.

  • Vapnik, V., 1995: The Nature of Statistical Learning Theory. Springer, 334 pp.

  • Vissa, N. K., P. C. Anandh, M. M. Behera, and S. Mishra, 2019: ENSO-induced groundwater changes in India derived from GRACE and GLDAS. J. Earth Syst. Sci., 128, 115, https://doi.org/10.1007/s12040-019-1148-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, A., D. P. Lettenmaier, and J. Sheffield, 2011: Soil moisture drought in China, 1950–2006. J. Climate, 24, 32573271, https://doi.org/10.1175/2011JCLI3733.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, W., W. Cui, X. Wang, and X. Chen, 2016: Evaluation of GLDAS-1 and GLDAS-2 forcing data and Noah model simulations over China at the monthly scale. J. Hydrometeor., 17, 28152833, https://doi.org/10.1175/JHM-D-15-0191.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weeink, W., 2010: Thresholds for flood forecasting and warning. M.S. thesis, Water Engineering and Management, University of Twente, 88 pp.

  • Woodward, D. E., R. H. Hawkins, R. Jiang, A. T. Hjelmfelt Jr., J. A. Van Mullem, and Q. D. Quan, 2003: Runoff curve number method: Examination of the initial abstraction ratio. World Water and Environmental Resources Congress, Philadelphia, PA, American Society of Civil Engineers, 691–700.

    • Crossref
    • Export Citation
  • Xing, W., W. Wang, Q. Shao, B. Yong, C. Liu, X. Feng, and Q. Dong, 2018: Estimating monthly evapotranspiration by assimilating remotely sensed water storage data into the extended Budyko framework across different climatic regions. J. Hydrol., 567, 684695, https://doi.org/10.1016/j.jhydrol.2018.10.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, R. A., C. A. Onstad, D. D. Bosch, and W. P. Anderson, 1989: AGNPS: A nonpoint-source model for evaluating agricultural watersheds. J. Soil Water Conserv., 44, 168173.

    • Search Google Scholar
    • Export Citation
  • Younis, S. M. Z., and A. Ammar, 2018: Quantification of impact of changes in land use-land cover on hydrology in the upper Indus Basin, Pakistan. Egypt. J. Remote Sens. Space Sci., 21, 255263, https://doi.org/10.1016/J.EJRS.2017.11.001.

    • Search Google Scholar
    • Export Citation
  • Yuan, X., Z. Ma, M. Pan, and C. Shi, 2015: Microwave remote sensing of short-term droughts during crop growing seasons. Geophys. Res. Lett., 42, 43944401, https://doi.org/10.1002/2015GL064125.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Estimation of Initial Abstraction for Hydrological Modeling Based on Global Land Data Assimilation System–Simulated Datasets

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  • 1 State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
  • 2 Tianjin Hydraulic Research Institute, Tianjin, China
  • 3 State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
  • 4 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
  • 5 State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
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Abstract

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jianzhu Li, lijianzhu@tju.edu.cn

Abstract

Initial abstraction (Ia) is a sensitive parameter in hydrological models, and its value directly determines the amount of runoff. Ia, which is influenced by many factors related to antecedent watershed condition (AWC), is difficult to estimate due to lack of observed data. In the Soil Conservation Service curve number (SCS-CN) method, it is often assumed that Ia is 0.2 times the potential maximum retention S. Yet this assumption has frequently been questioned. In this paper, Ia/S and factors potentially influencing Ia were collected from rainfall–runoff events. Soil moisture and evaporation data were extracted from GLDAS-Noah datasets to represent AWC. Based on the driving factors of Ia, identified using the Pearson correlation coefficient and maximal information coefficient, artificial neural network (ANN)-estimated Ia was applied to simulate the selected flood events in the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) model. The results indicated that Ia/S varies over different events and different watersheds. Over 75% of the Ia/S values are less than 0.2 in the two study areas. The driving factors affecting Ia vary over different watersheds, and the antecedent precipitation index appears to be the most influential factor. Flood simulation by the HEC-HMS model using statistical Ia gives the best fitness, whereas applying ANN-estimated Ia outperforms the simulation with median Ia/S. For over 60% of the flood events, ANN-estimated Ia provided better fitness in flood peak and depth, with an average Nash–Sutcliffe efficiency coefficient of 0.76 compared to 0.71 for median Ia/S. The proposed ANN-estimated Ia is physically based and can be applied without calibration, saving time in constructing hydrological models.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jianzhu Li, lijianzhu@tju.edu.cn
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