A kinematic wave based watershed model for soil erosion and sediment yield

Geospatially based distributed rainfall‐runoff modelling for simulation of internal and outlet responses in a semi‐forested lower Himalayan watershed

“…Before attempting calibration and field application of the developed model for the Pathri Rao watershed, it is considered essential to know the sensitivity of simulations to the model parameters related to soil texture and landuse. Following Foglia et al (2009) (who advocate that it is advantageous to consider local sensitivity analysis in model evaluation, possibly as a preliminary step to provide insights, such as identifying insensitive parameters), Jain et al (2004, 2005), Bathurst et al (2004), Nunes et al (2005), Rai and Mathur (2007), Al‐Qurashi et al (2008), Reddy et al (2008), Bhardwaj and Kaushal (2009) and Naik et al (2009), we have carried out a local sensitivity analysis as a preliminary step to provide insights. The sensitivity analysis was performed for the parameters, viz.…”

“…A number of such hydrological models with variety of structures and data requirements that are capable of simulating runoff, including soil erosion and sediment transport processes, are available in the literature (Singh and Woolhiser, 2002). Some of those are ANSWERS (Beasley et al , 1980), CREAMS (Knisel, 1980), SHETRAN (Ewen et al , 2000), an enhanced version of the SHE/SHESED model (Abbott et al , 1986a,b; Bathurst et al , 1995; Wicks and Bathurst, 1996); KYERMO (Hirshi and Barfield, 1988), WEPP (Nearing et al , 1989; Flanagan et al , 2007), AgNPS (Young et al , 1989), KINEROS‐2, an enhanced version of KINEROS (Woolhiser et al , 1990), LISEM (De Roo et al , 1996), SWAT (Arnold et al , 1998), EUROSEM (Morgan et al , 1998), and other models developed by Jain et al (2005), Rai and Mathur (2007), Reddy et al (2008), Naik et al (2009), among others.…”

“…,Bathurst et al (2004),Nunes et al (2005),Rai and Mathur (2007),Al-Qurashi et al (2008),Reddy et al (2008),Bhardwaj and Kaushal (2009) andNaik et al (2009), we have carried out a local sensitivity analysis as a preliminary step to provide insights. The sensitivity analysis was performed for the parameters, viz.…”

Combining an R-Based Evolutionary Algorithm and Hydrological Model for Effective Parameter Calibration

“…In addition, we are developing a tool similar as VIC-ASSIST [43], an auto-calibration tool, and will be available in the near future. update_MinSlopeOF <-pars [2] update_MinSlopeChBed <-pars [3] update_ChRoughness <-pars [4] update_CalCoefLCRoughness <-pars [5] update_CalCoefPorosity <-pars [6] update_CalCoefWFSuctionHead <-pars [7] update_CalCoefHydraulicK <-pars [8] update_CalCoefSoilDepth <-pars [9] ## Read '.gmp' file that contains information including parameter values and location of geophysical input data dat_gmp <-readLines("ds500_E201107.gmp") dat_gmp[72] <-paste("<IniSaturation>", update_IniSaturation, "</IniSaturation>", collapse = ", ", sep = "") dat_gmp[73] <-paste("<MinSlopeOF>", update_MinSlopeOF, "</MinSlopeOF>", collapse = ", ", sep = "") dat_gmp[74] <-paste("<MinSlopeChBed>", update_MinSlopeChBed, "</MinSlopeChBed>", collapse = ", ", sep = "") dat_gmp[76] <-paste("<ChRoughness>", update_ChRoughness, "</ChRoughness>", collapse = ",", sep = "") Box A1. Cont.…”

“…Moreover, it is difficult to incorporate various optimization algorithms into the modeling package when the number of parameters in the distributed hydrological model to be optimized is considerable. Therefore, previous studies have optimized the parameters of a distributed hydrological model using the trial-and-error method in cases where the required optimization algorithms were not included in the modeling package [6][7][8][9][10]. However, parameter calibration using the trial-and-error method can be time consuming; moreover, the reliability of the results is affected [3].…”