Analyze the impacts of afforestation on water availability as a consequence of climate transform, plus the effect of vegetation cover around the excellent from the simulation. Lastly, future function on smaller catchments will involve hybrid modeling (lumped hydrological modeling and machine mastering) [115] along with the use of machine studying tactics [110] to evaluate their efficiency overall performance in the simulation of maximum and minimum flows.Author Contributions: N.F.: Methodology; Formal Evaluation; Validation; Application; Writing–Original Draft; Visualization Preparation; Writing–Review and Editing. R.R.: Conceptualization; Methodology; Writing–Original Draft; Supervision. S.Y.: Methodology; Writing–Original Draft; Writing–Review and Editing. V.O.: Methodology; Software. P.R.: Writing–Review and Editing; Methodology. D.R.: Methodology; Writing–Review and Editing. F.B.: Conceptualization; Investigation; Writing–Original Draft Preparation; Writing–Review and Editing; Sources; Project Administration; Supervision. All authors have study and agreed to the published version from the manuscript. Funding: This analysis received no external funding. Institutional Critique Board Statement: Not applicable. Informed Consent Statement: Not applicable. Information Availability Statement: The data of this study are out there from the corresponding author upon reasonable request. Acknowledgments: The hydrometeorological and streamflow information for the study had been funded by Bioforest S.A. Also, we’re grateful for the help of CORFO Project 19BP-117424 “South Rivers Toolbox: Modelo predictor de la morfodin ica UCB-5307 Apoptosis fluvial para apoyar la gestion de cauces” during the improvement of your sensitivity evaluation in MATLAB. The authors wish to express their Pinacidil Purity thanks to the doctoral scholarship ANID-PFCHA/Doctorado Nacional/2021-21210861 for the assistance of F. Balocchi. D. Rivera thanks help from ANID/FONDAP/15130015. Conflicts of Interest: The authors declare no conflict of interest.Appendix ARivers Toolbox: Modelo predictor de la morfodin ica fluvial para apoyar la gestion de cauces” in the course of the development from the sensitivity analysis in MATLAB. The authors want to express their thanks to the doctoral scholarship ANID-PFCHA/Doctorado Nacional/2021-21210861 for the assistance of F. Balocchi. D. Rivera thanks support from ANID/FONDAP/15130015. Conflicts of Interest: The authors declare no conflict of interest.Water 2021, 13,Appendix A22 ofWater 2021, 13, x FOR PEER REVIEW24 of(D) X4 , for the GR4J hydrological model.Figure A1. Figure A1. Scatter plots in between the RMSE efficiency statistic (Y-axis) andthe parameter values: (A) (B) ,X2, (C) two ,three (C) X3 and Scatter plots in between the RMSE efficiency statistic (Y-axis) and also the parameter values: (A) X1, X1 (B) X X and (D) X4, for the GR4J hydrological model.Figure A2. Cont.Water 2021, 13,23 ofWater 2021, 13, x FOR PEER REVIEW25 ofFigure A2. Scatter plots involving the RMSE efficiency statistic (Y-axis) and also the parameter values: (A) X1 , (B) X2 , (C) X3 , Figure A2. Scatter plots among the RMSE efficiency statistic (Y-axis) along with the parameter values: (A) X1, (B) X2, (C) X3, (D) (D)X44and (E) X5,5 , for the GR5J hydrologicalmodel. X and (E) X for the GR5J hydrological model.Figure A3. Cont.Water 2021, 13,24 ofFigure A3. Scatter graphs between RMSE efficiency statistic (Y-axis) and parameter values: (A) X1 , (B) X2 , (C) X3 , (D) X4 , Figure A3. Scatter graphs in between RMSE efficiency statistic (Y-axis) and parameter values: (A) X1, (B) X2, (C) X3, (D) X4, (E.