# Extreme Droughs in the Peruvian Amazon Region (2000-2024)
## 4 Discussion
The influence of precipitation uncertainty is straightforward from Table 3, showing that, if using only ERA5 as precipitation data, the diagnosed four extreme droughts would have been 2023-24, 2022-23, 2015-16, and 2009-10: If using only MSWEP, they would have been 2023-24, 2004-05, 2009-10, and 2006-07, while using only CHIRPS, we would have obtained 2022-23, 2006-07, 2004-05, and 2023.24. The approach followed here to overcome this difficulty is only one of the different methods that can be followed. In the future, this problem must be revisited to apply a greater variety of datasets. The influence of the evapotranspiration estimation has not been addressed in this paper, but the use of a reanalysis variable instead of a fixed value addresses the influence of its spatio-temporal variability in the estimation of MCWD. As an example of the possible influence of this parameter, Figure 12 shows the difference in the estimations of average MCWD across study areas, obtained by subtracting the estimation under the assumption of a constant 100 mm value (as in most previous studies [1; 26; 54]) from the MCWD values obtained by estimating Et from ERA5. As can be seen, the three panels show that differences of more than 20 mm in average MCWD may arise from considering the Et variability, which can produce different diagnoses of drought state and category in different regions. The contribution of the ET variability in the estimation of MCWD is mostly positive in combination with MSWEP and CHIRPS precipitation data but mostly negative in combination with ERA5 data. The dependence on the year is not clear, so it must be concluded that the estimation of Et improves the MCWD estimations. The dependence of Et estimation on other databases will be addressed by the authors in the near future. Figure 11: Annual cycles of water deficit (WD) for the six study areas for the CHIRPS-ERA5 dataset. The right column of panels contains the WD distributions for the extreme and moderate drought hydraulic tears (HYs), and the left column contains the HYs preceding each drought HY. Another important issue that is worth more discussion is the use of MCWD to assess drought in the Peruvian Amazon region. The main argument is that it is a widely recognized and utilized metric for assessing drought conditions in the Amazon rainforest. By accounting for the cumulative difference between precipitation and evapotranspiration, MCWD provides an understanding of water stress over time, making it particularly valuable for studying the impacts of drought on tropical forest ecosystems. This is because MCWD explicitly quantifies the cumulative water deficit plants experience over dry periods, particularly during the dry season, which is critical for tropical forest functioning, granting leaf shedding, photosynthesis, and carbon uptake [26]. On the other hand, MCWD tracks the most intense water stress during the annual dry season, unlike SPEI or PDSI [61], which aggregate over various scales and may miss peak stress periods [30]. Nevertheless, other indices have also been used in Amazon drought studies, as is the case of [1], who applied the relative scPDSI in addition to relative MCWD, with comparable results, but scPSDI showed much less monthly variability than MCWD, as can be seen in Figure 6 of the referenced paper. The limitations of the standardized indices, such as SPEI and scPDSI, were summarized and discussed by [62], concluding that they are not recommended to describe drought situations but that they must be complemented by estimation of water stress. The MCWD index has been used by different authors to study key issues related to  
Figure 12: Difference in MCWD between the estimation of evapotranspiration by ERA5 and the assumption of constant Et = 100 mm for the forest in the years of the study period. (**A**) ERA5 Precipitation. (**B**) MSWEP precipitation. (**C**) CHIRPS precipitation. Amazon forest droughs, as [63; 64; 65].