Recently, studies are featuring larger datasets and an increasing number of hydrological events. Therefore, automated methods have become an indispensable requirement. In this work, an automated procedure to improve hysteresis analysis is proposed. Two main aspects are targeted for methodological improvement:  The qualitative and quantitative analysis axes of hysteresis loops. The concept of Hysteresis Signature, a sequence of parameters that defines the parts forming a hysteresis loop (linear, clockwise, and anticlockwise parts), is introduced to classify hysteresis shapes. A new numeric analysis format is also introduced following the signature to characterize each part of the loop individually. It uses both normalized and real-scale metrics for a full hysteresis quantification. The procedure was extensively tested and validated using 1400+ events from real datasets. Results showed a significant methodological improvement in both the qualitative and quantitative analysis aspects of a given hydrological hysteresis response. The automated classification identified 51 different shapes, successfully confirming the increase of identifiable hysteresis loops. The new numeric analysis format, compared to hysteresis indices, gives a unique quantitative examination in combined forms (e.g., Figure of eight loops) which improves the accuracy of the acquired information. Real-scale quantification improved normalized metrics by reflecting the physical impact associated with the opening of the loop. Finally, The overall results of the method’s testing showed that the procedure is versatile and compatible with varying data types so that the method can be generalized to various hysteresis relationships.