Analyze the impacts of afforestation on water availability on account of climate alter, and also the influence of vegetation cover on the high quality on the simulation. Ultimately, future work on tiny catchments will include hybrid modeling (lumped hydrological modeling and machine mastering) [115] as well as the use of machine mastering approaches [110] to evaluate their efficiency overall performance in the simulation of maximum and minimum flows.Author Contributions: N.F.: Methodology; Formal Analysis; Validation; Software; Writing–Original Draft; Visualization Preparation; Writing–Review and Editing. R.R.: Conceptualization; Methodology; Writing–Original Draft; Pinacidil In stock 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 read and agreed towards the published version from the manuscript. Funding: This analysis received no external funding. Institutional Assessment Board Statement: Not applicable. Informed Consent Statement: Not applicable. Information Availability Statement: The data of this study are out there in the corresponding author upon affordable request. Acknowledgments: The hydrometeorological and streamflow information for the study were funded by Bioforest S.A. Furthermore, we’re grateful for the help of CORFO Project 19BP-117424 “South Rivers Toolbox: Modelo predictor de la morfodin ica fluvial para apoyar la gestion de cauces” through the development from the sensitivity evaluation in MATLAB. The authors want to express their due to the doctoral scholarship ANID-PFCHA/Doctorado Nacional/2021-21210861 for the help 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” during the improvement in the sensitivity evaluation in MATLAB. The authors want to express their due to the doctoral scholarship ANID-PFCHA/Doctorado Nacional/2021-21210861 for the help 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 (Goralatide In stock Y-axis) andthe parameter values: (A) (B) ,X2, (C) 2 ,3 (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 among the RMSE efficiency statistic (Y-axis) along with the parameter values: (A) X1 , (B) X2 , (C) X3 , Figure A2. Scatter plots amongst the RMSE efficiency statistic (Y-axis) as well as 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 in between RMSE efficiency statistic (Y-axis) and parameter values: (A) X1 , (B) X2 , (C) X3 , (D) X4 , Figure A3. Scatter graphs between RMSE efficiency statistic (Y-axis) and parameter values: (A) X1, (B) X2, (C) X3, (D) X4, (E.