Understanding the dynamic nature of Time-to-Peak in UK streams

[1]  S. Sharifi,et al.  Methodology for Identifying the Best Equations for Estimating the Time of Concentration of Watersheds in a Particular Region , 2011 .

[2]  S. Mishra,et al.  A review of the synthetic unit hydrograph: from the empirical UH to advanced geomorphological methods , 2014 .

[3]  Asaad Y. Shamseldin,et al.  Peak flood estimation using gene expression programming , 2015 .

[4]  E. Kuichling,et al.  The Relation Between the Rainfall and the Discharge of Sewers in Populous Districts , 1889 .

[5]  L. Nandagiri,et al.  Assessment of variable source area hydrological models in humid tropical watersheds , 2018 .

[6]  R. D. Black,et al.  Partial Area Contributions to Storm Runoff in a Small New England Watershed , 1970 .

[7]  Bahram Gharabaghi,et al.  A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS , 2018 .

[8]  Phillip Jordan,et al.  Guidelines for rainfall-runoff modelling: towards best practice model application , 2011 .

[9]  R. Newcombe.  Two-sided confidence intervals for the single proportion: comparison of seven methods. , 1998, Statistics in medicine.

[10]  Graham W. Taylor,et al.  Prediction of flow duration curves for ungauged basins , 2017 .

[11]  Richard H. McCuen,et al.  Uncertainty Analyses of Watershed Time Parameters , 2009 .

[12]  T. Kjeldsen.  The revitalised FSR/FEH rainfall-runoff method , 2007 .

[13]  M. Hoseini,et al.  Estimating time of concentration in large watersheds , 2016, Paddy and Water Environment.

[14]  Amir Hossein Gandomi,et al.  Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems , 2012, Neural Computing and Applications.

[15]  Amir Hossein Zaji,et al.  A comparison of artificial intelligence-based classification techniques in predicting flow variables in sharp curved channels , 2019, Engineering with Computers.

[16]  P. Torma,et al.  Comparing Methods for Computing the Time of Concentration in a Medium-Sized Hungarian Catchment , 2016 .

[17]  T. Jackson,et al.  Sensitivity of SCS Models to Curve Number Variation , 1982 .

[18]  Tommy S. W. Wong.  Assessment of Time of Concentration Formulas for Overland Flow , 2005 .

[19]  F. Othman,et al.  The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models , 2014, Water Resources Management.

[20]  D. B. Thompson,et al.  Variations of time of concentration estimates using NRCS velocity method , 2007 .

[21]  X. Fang,et al.  Estimating time parameters of overland flow on impervious surfaces by the particle tracking method , 2015 .

[22]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[23]  T. Haktanir,et al.  Suitability of two-parameter gamma and three-parameter beta distributions as synthetic unit hydrographs in Anatolia , 1990 .

[24]  Maurizio Porfiri,et al.  Time of concentration: a paradox in modern hydrology , 2012 .

[25]  Arthur J. Askew,et al.  VARIATION IN LAG TIME FOR NATURAL CATCHMENTS , 1970 .

[26]  農業土木学会応用水文研究部会,et al.  応用水文 = Applied hydrology , 1991 .

[27]  R. McCuen,et al.  Estimating Urban Time of Concentration , 1984 .

[28]  Bahram Gharabaghi,et al.  A modified FAO evapotranspiration model for refined water budget analysis for Green Roof systems , 2018, Ecological Engineering.

[29]  I. Wu.  Design Hydrographs for Small Watersheds in Indiana , 1963 .

[30]  Theodore G. Cleveland,et al.  Literature Review on Timing Parameters for Hydrographs , 2005 .

[31]  Sandra B. Pavlovic,et al.  Discretization Issues in Travel Time Calculation , 2008 .

[32]  Hossein Bonakdari,et al.  A Highly Efficient Gene Expression Programming Model for Predicting the Discharge Coefficient in a Side Weir along a Trapezoidal Canal , 2017 .

[33]  R. H. Hawkins,et al.  Lag Time Characteristics for Small Watersheds in the U. S. , 1998 .

[34]  In Sopper.  Factors affecting the response of small watersheds to precipitation in humid areas , 2009 .

[35]  Theodore G. Cleveland,et al.  Time of Concentration Estimated Using Watershed Parameters Determined by Automated and Manual Methods , 2008 .

[36]  Bahram Gharabaghi,et al.  Integrative neural networks models for stream assessment in restoration projects , 2016 .

[37]  L. Watrud,et al.  Seasonal Soil Moisture Patterns in Contrasting Habitats in the Willamette Valley, Oregon , 2013 .

[38]  Geogg Pegram,et al.  A review of the regional maximum flood and rational formula using geomorphological information and observed floods , 2004 .

[39]  Bahram Gharabaghi,et al.  Prediction of wave runup on beaches using Gene-Expression Programming and empirical relationships , 2019, Coastal Engineering.

[40]  P. E. O'connell,et al.  River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood , 1970 .

[41]  Bahram Gharabaghi,et al.  Estimating 2-year flood flows using the generalized structure of the Group Method of Data Handling , 2019, Journal of Hydrology.

[42]  P. Armitage,et al.  Mid-P confidence intervals: a brief review , 1995 .

[43]  Hossein Bonakdari,et al.  An analysis of shear stress distribution in circular channels with sediment deposition based on Gene Expression Programming , 2017 .

[44]  Bahram Gharabaghi,et al.  Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals , 2019, Journal of Hydrology.

[45]  M. Simas.  Lag time characteristics in small watersheds in the United States , 1996 .

[46]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[47]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[48]  J. Smithers,et al.  Review of methods used to estimate catchment response time for the purpose of peak discharge estimation , 2014 .

[49]  Bahram Gharabaghi,et al.  Comparison of CANWET and HSPF for water budget and water quality modeling in rural Ontario , 2014 .

[50]  M. Shinoda,et al.  Seasonal change of soil moisture in Mongolia: its climatology and modelling , 2011 .

[51]  F. C. Bell,et al.  Characteristic response times in design flood estimation , 1969 .

[52]  D. Gray.  Synthetic Unit Hydrographs for Small Watersheds , 1961 .

[53]  T. Lim.  Predictors of urban variable source area: a cross‐sectional analysis of urbanized catchments in the United States , 2016 .

[54]  Bahram Gharabaghi,et al.  Entropy-based neural networks model for flow duration curves at ungauged sites , 2015 .

[55]  Günter Blöschl,et al.  Climate and catchment controls on the performance of regional flood simulations , 2011 .

[56]  Aleska Kaufmann Almeida,et al.  Performance of methods for estimating the time of concentration in a watershed of a tropical region , 2017 .

[57]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[58]  Bahram Gharabaghi,et al.  Reference Time of Concentration Estimation for Ungauged Catchments , 2018 .

[59]  J. Smithers,et al.  Direct estimation of catchment response time parameters in medium to large catchments using observed streamflow data , 2017 .

[60]  Bahram Gharabaghi.  Data for: Understanding the Dynamic Nature of Time-to-Peak in UK Streams , 2020 .

[61]  Gregory E. Granato,et al.  Estimating basin lagtime and hydrograph-timing indexes used to characterize stormflows for runoff-quality analysis , 2012 .

[62]  J. Smithers,et al.  Derivation and verification of empirical catchment response time equations for medium to large catchments in South Africa , 2016 .

[63]  Bahram Gharabaghi,et al.  Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques , 2019, Neural Computing and Applications.

[64]  Bahram Gharabaghi,et al.  Estimation of the Darcy–Weisbach friction factor for ungauged streams using Gene Expression Programming and Extreme Learning Machines , 2019, Journal of Hydrology.