Climate Change and Its Impacts on Streamflow: WRF and SCE-Optimized SWAT Models - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 411

Table 17.1 Sensitivity analysis ranking of 10 most sensitive parameters in SWAT model to stream

flow
Sensitivity
analysis order Parameter Description Parameter range

1 Cn2 Moisture condition II

curve no

35 98

2 Alpha Bf Baseflow recession

constant

0 1

3 Ch K2 Effective hydraulic

conductivity in main
channel

0:01 500

4 Surlag Surface runoff lag

coefficient

1 24

5 Ch N2 Manning n value for the

main channel

0:01 0.3

6 Blai Maximum potential leaf

area index for land
cover

0 8

7 Sol Awc Available water capacity 0 1

8 Esco Soil evaporation

compensation factor

0 1

9 Canmx Maximum canopy

storage

0 100

10 Gwqmn Threshold water level in

shallow aquifer for
base flow

0 5;000

for the model parameters which results in the minimum discrepancy between the
observed and the simulated discharge data. While manual calibration can be used
by trained, experienced users who are familiar with the model and the catchment
under consideration, auto-calibration is recommended especially for the new user.
Parameter Solution method (ParaSol) is a built-in auto-calibration model since
the SWAT 2005 version was implemented ( van Griensven and Meixner 2004 ).
ParaSol operates by a parameter search method for model parameter optimization
followed by a statistical method that was performed during the optimization to
provide parameter uncertainty bounds and the corresponding uncertainty bounds
on the model outputs. The ParaSol method aggregates objective functions (OF)
into a global optimization criterion (GOC), minimizes these OF's or a GOC
using the Shuffled Complex Evolution Method (SCE-UA) algorithm and performs
uncertainty analysis with a choice between two statistical concepts. The SCE-
UA ( Duan et al. 1992 ) method is based on a synthesis of all the best functions
from many other existing methods consisting of the Genetic Algorithm (GA),
simplex method ( Nelder and Mead 1965 ), controlled random search ( Price 1987 ),
competitive evolution ( Holland 1975 ) and the newly developed concept of complex
shuffling. SCE-UA conducts a global minimization of a single function for up to 16
parameters. This method is also capable for non-linear optimization problems.