Adding new sites was advantageous but we did not observe major differences in model performance for particular new site locations.
We show that accounting for distance-dependent survival substantially improves the model performance for three tested seed dispersal kernels.
The main goal was to identify proper sets of hydraulic parameters and to evaluate their relevance on hydrological model performance for irrigation management purposes.
In addition, the evaluation is also focused on the model performance for TEC variations due to local solar time, geographical locations, and long-term trend, or drift.
It was observed that the SVM model performance for the multi-layer soil moisture estimation can be influenced by the rainfall magnitude (e.g., dry and wet spells).
We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.
Our calibration outputs for monthly simulation for the period from 1976 to 1984 showed a good model performance for flow rates with NSE and PBIAS values of 0.76 and −11.80, respectively; also a good model performance for sediment concentration with NSE and PBIAS values of 0.69 and 7.12, respectively.
However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32 0.52, PBIAS: ±32.73%, and RSR: 0.63 0.82).
Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59 0.61, PBIAS: ±13.70%, and RSR: 0.63 0.64) while the evapotranspiration estimations were improved.
With the exception of Blue River basin, the overall model performance for the validation period remains good to very good, indicating that the model is able to simulate the relevant hydrologic processes in the basins accurately.
Fivefold cross-validation showed best model performance for POC with a Pearson's correlation coefficient of 0.91, while predictions for SSC and PN achieved a satisfying correlation of 0.86 and 0.87, respectively.
