Analyze the impacts of afforestation on water availability on account of climate transform, and also the impact of vegetation cover around the excellent in the simulation. Ultimately, future operate on small catchments will involve hybrid modeling (lumped hydrological modeling and machine understanding) [115] along with the use of machine finding out tactics [110] to evaluate their efficiency efficiency in the simulation of maximum and minimum flows.Author Contributions: N.F.: Methodology; Formal Analysis; Validation; Software program; 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; Resources; Project Administration; Supervision. All authors have read and agreed towards the published version on the manuscript. Funding: This analysis received no external funding. Institutional Assessment Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data of this study are available in the corresponding author upon reasonable request. Acknowledgments: The hydrometeorological and streamflow information for the study have been 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” for the duration of the development with the sensitivity analysis in MATLAB. The authors wish to express their because of 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.Appendix ARivers Toolbox: Modelo predictor de la morfodin ica fluvial para apoyar la gestion de cauces” for the duration of the development on the sensitivity analysis in MATLAB. The authors wish to express their because of 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.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 among the RMSE efficiency PHA-543613 Purity statistic (SC-19220 custom synthesis Y-axis) andthe parameter values: (A) (B) ,X2, (C) two ,three (C) X3 and Scatter plots between the RMSE efficiency statistic (Y-axis) along with 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 the parameter values: (A) X1 , (B) X2 , (C) X3 , Figure A2. Scatter plots involving the RMSE efficiency statistic (Y-axis) and the parameter values: (A) X1, (B) X2, (C) X3, (D) (D)X44and (E) X5,five , for the GR5J hydrologicalmodel. X and (E) X for the GR5J hydrological model.Figure A3. Cont.Water 2021, 13,24 ofFigure A3. Scatter graphs amongst RMSE efficiency statistic (Y-axis) and parameter values: (A) X1 , (B) X2 , (C) X3 , (D) X4 , Figure A3. Scatter graphs involving RMSE efficiency statistic (Y-axis) and parameter values: (A) X1, (B) X2, (C) X3, (D) X4, (E.
