transfR: an open-source R package for streamflow prediction in ungauged catchments 

<div> <p><span>This R package aims to technically bring together different modelling tools for the estimation of streamflow time series of ungauged outlets. It allows implementing a spatial interpolation of streamflow of neighbouring gauged basins using a geomorphology-based deconvolution-convolution modelling approach. The robustness of the methodology has been demonstrated in several hydro-climatic contexts through several publications over the last years. However, the numerical tool itself was not easily accessible to all. The recent public availability of this package aims to facilitate an application by end-users (in particular water and basin managers, public authorities and engaged citizens). We also wish to obtain feedback and enable new bridges between science and practice as well as research on the method to explore its generality and flexibility in various contexts. </span><span> </span></p> </div><div> <p><span>The hydrological modelling itself is based on the description of the hydro-geomorphometry of the river drainage network which can be easily observed for any given outlet. An inversion of this model for the basin with a gauged outlet allows the observed streamflow to be deconvoluted and the signal of water flowing into the rivers from the slope (the net rainfall) to be estimated. Transferring this estimate of the net rainfall series to the basin of a targeted ungauged outlet thus allows the flow series to be simulated there. A spatial analysis of the hydrological distances between catchments allows the observed streamflow time series from several gauged catchments to be strategically combined to increase the robustness of the prediction. </span><span> </span></p> </div><div> <p><em>https://CRAN.R-project.org/package=transfR </em></p> </div><div> <p><strong>References: </strong></p> </div><div> <p><span>Boudhraâ H., Cudennec C., Andrieu H., Slimani M., 2018. </span><em><span>Net rainfall estimation by the inversion of a geomorphology-based transfer function and discharge deconvolution.</span></em><span> Hydrological Sciences Journal, 63, 2, 285-301, </span><span>http://dx.doi.org/10.1080/02626667.2018.1425801</span><span> </span><span> </span></p> </div><div> <p><span>de Lavenne A., Cudennec C., 2019. </span><em><span>Assessment of freshwater discharge into a coastal bay through multi-basin ensemble hydrol</span></em><em><span>ogical modelling. </span></em><span>Science of the Total Environment, 669, 812-820, </span><span>https://doi.org/10.1016/j.scitotenv.2019.02.387</span><span> </span><span> </span></p> </div><div> <p><span>de Lavenne A., Skøien J.O., Cudennec C., Curie F., Moatar F., 2016. </span><em><span>Transferring measured discharge time-series: large-scale comparison of Top-kriging to geomorphology-based inverse modeling.</span></em><span> Water Resources</span><span> </span><span>Research, 52, 7, 5555-5576, </span><span>http://dx.doi.org/10.1002/2016WR018716</span><span>.</span><span> </span></p> </div><div> <p><span>Ecrepont S., Cudennec C., Anctil F., Jaffrézic A., 2019. </span><em><span>PUB in Québec: A robust geomorphology-based </span></em><em><span>deconvolution-reconvolution framework for the spatial transposition of hydrographs.</span></em><span> Journal of Hydrology, 570, 378-392, </span><span>https://doi.org/10.1016/j.jhydrol.2018.12.052</span><span> </span></p> </div>