ABSTRACT
Simulation of complex hydrological responses in large watersheds over years has prompted the need
for procedures for autocalibration. The commonly available models of watershed hydrology are of the
event type applicable on a basin scale or continuous models applicable on a field scale. The
Watershed Resources Management (WRM) model is a basin-scale model for continuous simulation. It
is generally applicable in planning, forecasting and operational hydrology, to the study of
environmental impacts of land-use change and to soil and water conservation planning. Empirical
equations, derived from relating physical quantities experimentally and validated independently, are
employed. In every hydrological simulation, there is always a need for optimization and the
optimization is carried out by the best possible technique that will yield perfect or near-perfect values
for selected calibration parameters. WRM model was originally applied to Canadian conditionsand
was heuristically optimized for that application. In this work, a modified WRM version was
embedded in normal and autocalibration modes. The normal mode does simulation without
optimization of parameters, while the autocalibration mode runs with optimization of parameters.
Theoptimization method adopted is Genetic Algorithm (GA), which is an Artificial Intelligence-based
methodology for solving problemsemploying non-mathematical, non-deterministic, but stochastic
process or algorithm. Four parameters with high sensitivity were usedinthe autocalibration process,
namely, theManning roughness coefficient for land surface(MANN1),Manning roughness coefficient
for stream surface (MANN2),Manning roughness coefficient for terrace surface (MANN3) and a
surface retention parameter (KRET). These parameters were used for calibration using WRMGA and
WRMGUI software developed in this study. Genomes were generated within specified ranges using
random number generator. The generated values were stored in a file, Optimized. dat, which the
WRMGA software calls up and uses to compute the best fit. Hydrograph plots of both the original
heuristically calibrated simulations for Canadian watersheds and the autocalibrated simulations for the
same watersheds were compared with measured hydrographs, and statistically analysed. WRM model
originally calibrated to the watersheds gave a regression coefficient (R) of 34.8 % while the
autocalibrated model gave 37 %. This result shows an improvement of 2.2 % by the autocalibration
scheme. However, autocalibration involves a more objective procedure that can be employed by the
non-expert in hydrologic modelling. To make for user-friendliness, the original WRM model coded in
FORTRAN was translated to C-sharp (C#). The WRM model was successfully repackaged for
autocalibration in this work.
, U & OKWUCHUKWU, L (2021). Development Of Autocalibraton Capability For Watershed Resources Management (Wrm) Model. Afribary. Retrieved from https://track.afribary.com/works/development-of-autocalibraton-capability-for-watershed-resources-management-wrm-model
, UZOIGWE and LUKE OKWUCHUKWU "Development Of Autocalibraton Capability For Watershed Resources Management (Wrm) Model" Afribary. Afribary, 05 May. 2021, https://track.afribary.com/works/development-of-autocalibraton-capability-for-watershed-resources-management-wrm-model. Accessed 13 Nov. 2024.
, UZOIGWE, LUKE OKWUCHUKWU . "Development Of Autocalibraton Capability For Watershed Resources Management (Wrm) Model". Afribary, Afribary, 05 May. 2021. Web. 13 Nov. 2024. < https://track.afribary.com/works/development-of-autocalibraton-capability-for-watershed-resources-management-wrm-model >.
, UZOIGWE and OKWUCHUKWU, LUKE . "Development Of Autocalibraton Capability For Watershed Resources Management (Wrm) Model" Afribary (2021). Accessed November 13, 2024. https://track.afribary.com/works/development-of-autocalibraton-capability-for-watershed-resources-management-wrm-model