Abstract: The natural flow regime of rivers across the world has been largely modified. Understanding the extent to which the flow regime deviates from natural conditions is necessary for designing sound management and restoration measures. In this regard, ?Indicators of Hydrologic Alteration? is currently considered one of the most effective approaches for assessing hydrologic alteration (HA). However, several generalized drawbacks such as the climatic variability between the pre- and post-impacted series and the scarcity of hydrological data in many impaired rivers should be addressed. In this study, a protocol with the following five alternative designs based on data availability is presented: (1) Paired-Before?After Control?Impact (BACIP), (2) Before?After (BA), (3) Control?Impact (CI), (4) Hydrological Classification (HC) and (5) Predicted Hydrological indices (HP). BACIP compares the status of the impacted gauge before and after the perturbation is started, in addition to controlling for natural climatic changes. Hence, it has been considered as the reference benchmark for all other designs. When this protocol was applied to 11 reservoirs situated in the northern third of the Iberian Peninsula, the BA design was able to correctly identify most of the non-significant HA but failed in almost one quarter of the significant alterations. Similarly, BACIP and CI showed an agreement of >80%. This suggests that the method is suitable when proper data are unavailable for BACIP or BA. In addition, our results indicated that the critical thresholds for HA varied depending on the hydrological index being considered. Significant HAs ranged from <5% for the number of days with increasing and decreasing flows to >64% for the duration of low-flow pulses. To delineate adequate thresholds, further research combining hydrological analyses with the biological response to the HA is warranted. Finally, the application of HC and HP designs revealed a significant degree of uncertainty related to the intra-class variability and the predictive error of the models. Therefore, 25% of the analysis could not be evaluated. However, in the evaluable cases, the HC and HP designs correctly assessed >75% of the HA, which highlighted the potential of this method in cases of scarce streamflow data.

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