Dear Editor, we have revised our work and manuscript in order to address comments and suggestions of the three reviewers. ---RESPONSES TO COMMENTS OF REVIEWER #1--- GENERAL COMMENT

In this study, the ability of five topographic indices to predict the gully trajectories observed in two adjacent watersheds located in Sicily (Italy) was evaluated. Two of these indices, named MSPI and MTWI, as far as we know, have never been employed to this aim. They were obtained by multiplying the stream power index (SPI) and the topographic wetness index (TWI), respectively, by the convergence index (CI). The predictive ability of the topographic indices was measured by using both cut-off independent (AUC: area under the receiver operating characteristic curve) and dependent statistics (Cohen's kappa index κ, sensitivity, specificity). These statistics were calculated also for 100 MARS (multivariate adaptive regression splines) and 100 LR (logistic regression) model runs, which used as predictors the topographic variables (i.e. contributing area, slope steepness, plan curvature and convergence index) combined into the five indices. Performance statistics of both topographic indices and statistical models were calculated using 100 random samples of 2 m grid cells, which were extracted only from flow concentration lines. This was done in order to focus the validation process on where gully erosion is more likely to occur. MSPI achieved the best predictive skill (AUC > 0.93; κ > 0.71) among the topographic indices and exhibited similar and better accuracy than local (i.e. trained and validated in the same watershed) and transferred (i.e. trained in one watershed and tested in the other one) LR models, respectively. On the other hand, MSPI performed similarly to transferred MARS runs (AUC > 0.92; κ > 0.71) but slightly worse than local MARS runs (AUC > 0.95; κ > 0.77). Based on the results of this experiment, it can be inferred that (i) including CI helps in detecting hollow areas where gullies are more likely to occur and (ii) MPSI can be a valid alternative to a data driven approach for mapping gully erosion susceptibility in areas where a gully inventory is not available, which is necessary to calibrate statistical models. Research Data Related to this Submission -------------------------------------------------There are no linked research data sets for this submission. The following reason is given: Data will be made available on request Dear Editor, we have revised our work and manuscript in order to address comments and suggestions of the three reviewers. ---RESPONSES TO COMMENTS OF REVIEWER #1--GENERAL COMMENT This is a well-written article with clearly defined objectives, methods, results, and discussion. The study presents statistical and geospatial evaluation of the predictive performance of five topographic index models and two statistical modeling approaches. In addition to three standard TI models, two modified models were introduced by adding a convergence index CI to SPI and TWI models. This addition seemed to improve the gully predictions by adding sort of a "weighting" factor to each cell that would enforce channel incision. While this modification did not improve the TWI model's prediction, it drastically enhanced the prediction ability of the SPI model. The TWI model has a logarithmic form while the SPI model is a simple two factor product. The two statistical models (LR and MARS) unsurprisingly outperformed the TI models due to their extensive calibration on topographic attributes prior to their application to the watershed. It was interesting to learn that the thresholds for topographic index models and occurrence statistics were found to be in the range of the ones reported for Kansas, USA. With different soil types, land management, rainfall characteristics, etc, in two regions of the world, basic topographic features (slope, contributing area, curvature, convergence) appeared to yield similar results. Gullies provide sources of extensive erosion and their placement in the watershed is not yet fully understood, a development of predictive techniques is of paramount importance for watershed management and planning. I recommend the article to be published after minor comments below are addressed. RESPONSE Dear Reviewer, the authors wish to thank you for having read carefully our work and for having appreciated, as well as for your valuable suggestions. We have modified our manuscript to address your suggestions. COMMENT #1 L. 148: "combine two or more primary topographic attributes". Which ones? Recommend providing examples. RESPONSE #1 We added: “including contributing area, slope steepness, plan curvature and convergence index agree”. These are the primary topographic attributes which were combined. COMMENT #2 L. 149: Any topographic index modelling heavily relies on the quality of DEM. A more specific description of the locally-developed Lidar-based DEM would be appreciated, the URL refers to a site in Italian. RESPONSE #2 We agree. We added more details, providing information about vertical accuracy of the DEM. Moreover, we provided a new URL (WMS server) which can be used to load the DEM in a GIS software. COMMENT #3 L. 197: "where CI (m) is the convergence index (Köthe et al., 1996). " Although the basic description of the CI index is presented, the mathematical definition remains unclear. The citation refers to publication in German with no English translation. Since this index provides a significant improvement to the SPI model, providing mathematical and possibly graphical representation would be very helpful. RESPONSE #3 Response to Reviewers