# Extreme Droughs in the Peruvian Amazon Region (2000-2024)
## 5 Summary and Conclusions  
Precipitation, evaporation, and energy flux records from different data sources have been analyzed for the period 2000-2024 to investigate the intensity and extension of major droughs in the Peruvian Amazon. The sources of data for the identification of the presence of drought conditions were ERA5 reanalysis for evapotranspiration, precipitation and surface fluxes and precipitation datasets CHIRPS and MSWEP. To evaluate drought conditions, the Maximum Cumulative Water Deficit (MCWD) index was used, based on the difference between precipitation, and evaporation records. Differences were found in the extreme drought hydrologic years identified with the three datasets, so the statistical distribution of the average values of MCWD across the datasets was considered to conclude that the hydrologic years of 2004-05, 2009-10, 2022-23, and 2023-24 were classified as extreme droughs, while 2006-07 and 2015-16 were classified as moderate droughs. Regarding the area covered by different drought thresholds, it also depends on the applied dataset. Using the estimations of the ERA5 and CHIRPS, the lower threshold (MCWD \(\geq\) 30 mm) attained around 80% of the area of the region, and the higher threshold (MCWD \(\geq\) 200 mm) reached around 20%. The hydrologic year with the most extended maximum threshold, using ERA5 and CHIRPS, was 2023-24, but according to MSWEP, it corresponded to 2004-05 and 2009-10. The moderate drought of 2006-07 had not been identified before, and that of 2015-16 had been generally considered an extreme drought for the entire Amazon basin. However, this 2015-16 drought was studied by [PERSON] et al. (2016) [23], who found that high precipitation rates were recorded in part of the Peruvian Amazon, which gave a dry-wet bipolar characteristic to this HY for Peru, consistent with our classification as moderate drought for the Peruvian Amazon region. Six study areas inside the Peruvian Amazon were defined, including boxes of 2 \(\times\) 2 degrees in northeastern Loreto (LOR1), northwestern Loreto (LOR2), southeastern Loreto (LOR3), the Moyobamba (MOY) area, including areas of the Peruvian Amazon regions of San Martin, Amazonas, and the easternmost part of Loreto, and two other boxes in Ucayali (UCA), at the central part of the Peruvian Amazon region, and Madre de Dios(MD), at the southern part of the region. Time series of precipitation, evaporation, and MCWD were constructed for each. In general, all the databases showed similar drought patterns, with most of the water deficit concentrated between June and August, focusing on the higher values of MCWD in the Ucayali and Madre de Dios regions, but they differed in the degree of extension of severe drought conditions to the Andes-Amazon transition region east of the Andes. This can be explained by considering the possible overestimation of precipitation by ERA5 in mountain regions. A contribution of this paper is the evaluation of the predominant weight of the drought conditions in the southern areas of Peru and the forest regions in the Andes-Amazon transition region relative to the northern part of the Peruvian Amazon, which has been much less affected, except for the most recent drought period. It is important to underline the weight of the uncertainty of the precipitation dataset in the Amazon drought diagnosis, considering the scarcity of measurements. This problem has been approached here with the use of three different datasets but will be revisited in the future, together with the influence of the uncertainty caused by the evapotranspiration data sources. Different drought indices will also be applied in future work to the diagnosis of drought conditions in the Peruvian Amazon region to compare with the present results. Conceptualization, D.M.-C., K.T. and J.-C.E. ; methodology, D.M.-C. and M.O.A. ; software, D.M.-C., M.O.A. and A.V.-L.; validation, D.M.-C. and M.O.A. ; formal analysis, D.M.-C., K.T., J.-C.E. and M.O.A. ; investigation, D.M.-C., K.T., [PERSON]-C.E. and M.O.A. ; resources, F.Y.S. and K.T.