<p>The magnitude and frequency of extreme precipitation events are expected to increase in central Peruvian Andes for this century, which will pose a significant challenge on water resources management and flood risk mitigation. The present study focuses on assessing the possible flood hazard under two different climate change scenarios (SSP 4.5 and SSP 8.5) in the lower part of the Lurin River watershed (~ 1642.5 Km<sup>2</sup>) by using a distributed physically-based hydrologic and erosional model (e.g. TREX) and a 2-D depth-averaged hydraulic and sediment transport model (e.g. BASEMENT-2D). The models were calibrated using hydrometeorological data corresponding to the extreme flood events of 2015 and 2017 and satellite-based and UAV-derived inundation maps. Future climate scenarios are going to be constructed from bias-corrected outputs of CMIP6 global climate models, while the rainfall temporal patterns for different return periods will be obtained from observed precipitation events corresponding to extreme flood events of El Ni&#241;o 2017. Results are expected to provide important data needed to make policy changes to mitigate the negative impacts of climate change in the Lurin River basin.&#160;</p>
<p>In recent years, there has been an increasing interest in estimate future conditions on biomes and aridity due to climate change. Using a new observed-based gridded dataset and remote sensing products, we evaluate the future features in terms of potential biomes (PB) and aridity index (AI) over Peru.&#160;</p><p>Ten PBs were established for the present conditions by grouping the ecosystems maps at the national scale. The map presents biomes within areas from 1.08 to 42.44% of total coverage. In order to handle imbalanced data, we designed a calibration and validation scheme for three machine learning algorithms (Random Forest, SVM, and KNN) as follow: first, we perform a gridded search for the best parameters of each model; second, we tested the robustness of each model with a cross validations, checking their f1 score, the confusion matrix and the weighted average precision-recall; finally, we performed a cost-sensitive learning to make more suitable the learning approach for very imbalanced data. The best model is going to be used to predict future conditions of PB. For AI, we evaluate the present trend and quantified the contributions of climate variables to Ai variations. Also, the relationship between AI and vegetative greening was explored. The future change of AI is seen by its spatial variation (migration) of the dryland subtypes.</p><p>The preliminary results showed that random forest worked best for the PB imbalanced data, having a 0.84 weighted average in precision and recall metric. The model reproduces 9 of the PB with low error 4.5% and overestimates 34.52 % one of them in the Amazon. Furthermore, there is an increasing slight trend (not significant) of AI at the drainage-scale, mainly in the Pacific. We hypothesize that there is a migration of dryland subtypes from dry to wet areas in the present time.&#160;</p><p>This research is part of the project &#8220;Apoyo a la Gesti&#243;n del Cambio Climatico 2da. Fase&#8221; financed by The Swiss Agency for Development and Cooperation (SDC).</p>
In soil erosion estimation models, the variable with the greatest impact is rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000-2020. By using this method, a correlation of 0.7 was found between the PISCO\_reed and RE obtained by the observed data. An average annual RE for Peru of 4831 MJ·mm·ha-1·h-1 was estimated with a general increase towards the lowland Amazon regions and high values are found on the north-coast Pacific area of Peru. The spatial identification of the most risk areas of erosion, was carried out through a relationship between the ED and rainfall. Both erosivity data sets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.
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