Development Of Autocalibraton Capability For Watershed Resources Management (Wrm) Model

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.