EGU2020-20432, updated on 29 Dec 2023
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Climate change impacts on biomes and aridity in Peru

Jose Augusto Zevallos Ruiz1,2,3, Adrian Huerta1,4, Waldo Lavado1, Evelin Sabino1, Fiorella vega1, and Oscar Felipe1
Jose Augusto Zevallos Ruiz et al.
  • 1Servicio Nacional de Meteorología e Hidrología del Peru
  • 2Pontificia Universidad Católica del Perú, Peru (
  • 3Universidad Tecnologica del Perú
  • 4Universidad Nacional Agraria La Molina

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. 

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.

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. 

This research is part of the project “Apoyo a la Gestión del Cambio Climatico 2da. Fase” financed by The Swiss Agency for Development and Cooperation (SDC).

How to cite: Zevallos Ruiz, J. A., Huerta, A., Lavado, W., Sabino, E., vega, F., and Felipe, O.: Climate change impacts on biomes and aridity in Peru, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20432,, 2020.