Climate Change Impact on Peruvian Biomes

Analysis of changes in temperature and precipitation in South American countries and ecoregions: Comparison between reference conditions and three representative concentration pathways for 2050

Climate change is a global concern, and its impact on environmental variables such as temperature and annual precipitation is unknown spatially in the desert, andes, and rainforest ecoregions of Peru, Ecuador, and Colombia. In this study, we conducted a general review of climate drivers for South America (SA) and explored climate data using the GCM compareR package (General Circulation Models) and average ensembles for temperature and precipitation. Our results showed that all GCMs demonstrated increases in the annual mean temperature (BIO1) and in the mean temperature of the driest quarter (BIO9) for Peru, Ecuador, and Colombia for 2050 in three RCPs (2.6, 4.5, and 8.5). Also, most of the GCMs showed increases in the annual precipitation (BIO12) and the precipitation in the driest quarter (BIO17). We conducted non-parametric tests (Kruskal-Wallis Test) to assess if the medians of temperature and precipitation in the three ecoregions are equal for both the baseline and the climate change scenarios. We rejected the null hypothesis that the medians are equal for both temperatures and precipitation in the baseline vs. 2050 RCPs (2.6, 4.5, and 8.5). A spatial analysis was conducted to visualize the variations in temperature and precipitation between the RCPs versus the baseline, and the spatial variation at the country or ecoregion level can be observed. The annual mean temperature (°C) or annual precipitation (mm) divided by its standard deviation for each ecoregion (M metric) was analyzed to see how much the average temperature or the annual precipitation is relatively large compared to the variability or dispersion of temperatures or precipitation respectively; the average temperature and the annual precipitation for the baseline and the three RCPs are relatively large and associated with the variability or dispersion of their temperatures in the Napo moist forest compared to the other ecoregions. Our study provides important insights into the potential impacts of climate change on these ecosystems. Prospects in the Napo moist forest ecoregion, where significant changes in temperature and humidity have already occurred, and new species have invaded or evolved in the western Amazon rainforest, are particularly highlighted and reflected in terms of risk mitigation, ecosystem restoration, surveillance, and monitoring.

High-resolution grids of daily air temperature for Peru - the new PISCOt v1.2 dataset

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981-2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation

The global potential distribution of biomes (natural vegetation) was modelled using 8,959 training points from the BIOME 6000 dataset and a stack of 72 environmental covariates representing terrain and the current climatic conditions based on historical long term averages (1979-2013). An ensemble machine learning model based on stacked regularization was used, with multinomial logistic regression as the meta-learner and spatial blocking (100 km) to deal with spatial autocorrelation of the training points. Results of spatial cross-validation for the BIOME 6000 classes show an overall accuracy of 0.67 and R²_logloss of 0.61, with "tropical evergreen broadleaf forest" being the class with highest gain in predictive performances (R²_logloss = 0.74) and "prostrate dwarf shrub tundra" the class with the lowest (R²_logloss = -0.09) compared to the baseline. Temperature-related covariates were the most important predictors, with the mean diurnal range (BIO2) being shared by all the base-learners (i.e.,random forest, gradient boosted trees and generalized linear models). The model was next used to predict the distribution of future biomes for the periods 2040-2060 and 2061-2080 under three climate change scenarios (RCP 2.6, 4.5 and 8.5). Comparisons of predictions for the three epochs (present, 2040-2060 and 2061-2080) show that increasing aridity and higher temperatures will likely result in significant shifts in natural vegetation in the tropical area (shifts from tropical forests to savannas up to 1.7 ×10⁵ km² by 2080) and around the Arctic Circle (shifts from tundra to boreal forests up to 2.4 ×10⁵ km² by 2080). Projected global maps at 1 km spatial resolution are provided as probability and hard classes maps for BIOME 6000 classes and as hard classes maps for the IUCN classes (six aggregated classes). Uncertainty maps (prediction error) are also provided and should be used for careful interpretation of the future projections.

An integrated approach to seismic risk assessment using random forest and hierarchical analysis: Pisco, Peru

As Peru is subject to large seismic movements owing to its geographic condition, determining seismic risk levels is a priority task for designing appropriate management plans. These actions become especially relevant when analyzing Pisco, a Peruvian city which has been heavily affected by various seismic events through the years. Hence, this project aims at estimating the associated seismic risk level and its previous requirements, such as hazard and vulnerability. To this end, a hybrid approach of machine learning (i.e., Random Forest) and hierarchical analysis (i.e., the Saaty matrix) was used. Risk levels were calculated through a double-entry table that establishes the relation between hazard and vulnerability levels. Results suggest that the city of Pisco exhibits both medium (lower city areas) and high (higher city areas) hazard levels in similar proportion. In addition, the coast area is considered a very-high hazard zone. Regarding vulnerability, the central area of the city exhibits a medium vulnerability level, whereas the periphery denotes high and very-high vulnerability levels. The interrelation of these components results in overall high-risk levels, with very-high levels in some central areas of the city. Finally, the results from this research study are expected to be useful for the authorities in charge of fostering specific activities in each sector and, simultaneously, as a motivator for future studies within this field.

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Implementation of RF for assessing seismic hazard.

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Implementation of AHP for assessing seismic vulnerability.

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The hazard level in Pisco is similar and constant in the short term.

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High and very-high risk zones have been identified in the city of Pisco.

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Pisco’s seismic risk level is sensitive to the vulnerability of its population.

Regeneration of Pinus sibirica Du Tour in the Mountain Tundra of the Northern Urals against the Background of Climate Warming

Climate is one of the key drivers of the plant community’s structure and trends. However, the regional vegetation-climate features in the ecotone have not yet been sufficiently studied. The aim of the research is to study features of Pinus sibirica Du Tour germination, survival, and growth in the mountain tundra of the Northern Urals against the background of a changing climate. The following research objectives were set: To determine the abundance and age structure of P. sibirica undergrowth on the mountain tundra plateau, identify the features of P. sibirica growth in the mountain tundra, and examine the correlation between the multi-year air temperature pattern, precipitation, and P. sibirica seedling emergence. A detailed study of the Pinus sibirica natural regeneration in the mountain stony shrub-moss-lichen tundra area at an altitude of 1010–1040 m above sea level on the Tri Bugra mountain massif plateau (59°30′ N, 59°15′ E) in the Northern Urals (Russia) has been conducted. The research involved the period between 1965 and 2017. Woody plant undergrowth was considered in 30 plots, 5 × 5 m in size. The first generations were recorded from 1967–1969. The regeneration has become regular since 1978 and its intensity has been increasing since then. Climate warming is driving these processes. Correlation analysis revealed significant relationships between the number of Pinus sibirica seedlings and the minimum temperature in August and September of the current year, the minimum temperatures in May, June, and November of the previous year, the maximum temperatures in May and August of the current year, and precipitation in March of both the current and previous years. However, the young tree growth rate remains low to date (the height at an age of 45–50 years is approximately 114 ± 8.8 cm). At the same time, its open crowns are rare single lateral shoots. The length of the side shoots exceeds its height by 4–5 times, and the length of the lateral roots exceeds its height by 1.2–1.5 times. This is an indicator of the extreme conditions for this tree species. With the current rates of climate warming and the Pinus sibirica tree growth trends, the revealed relationships allow for the prediction that in 20–25 years, the mountain tundra in the studied Northern Urals plateau could develop underground-closed forest communities with a certain forest relationship. The research results are of theoretical importance for clarifying the forest-tundra ecotone concept. From a practical point of view, the revealed relationship can be used to predict the trend in forest ecosystem formation in the mountain forest-tundra ecotone.

Experience of Forest Ecological Classification in Assessment of Vegetation Dynamics

Due to global climate change and increased forest transformation by humans, accounting for the dynamics of forest ecosystems is becoming a central problem in forestry. We reviewed the success of considering vegetation dynamics in the most influential ecological forest classifications in Russia, the European Union, and North America. Out of the variety of approaches to forest classification, only those that are widely used in forestry and forest inventory were selected. It was found that the system of diagnostic signs developed by genetic forest typology based on the time-stable characteristics of habitats as well as the developed concept of dynamic series of cenosis formation allows us to successfully take into account the dynamics of vegetation. While forest dynamics in European classifications is assessed at a theoretical level, it is also possible to assess forest dynamics in practice due to information obtained from EUNIS habitat classification. In ecological classifications in North America, the problem of vegetation dynamics is most fully solved with ecological site description (ESD), which includes potential vegetation and disturbance factors in the classification features. In habitat type classification (HTC) and biogeoclimatic ecosystem classification (BEC), vegetation dynamics is accounted based on testing the diagnostic species and other signs of potential vegetation for resistance to natural and anthropogenic disturbances. Understanding of vegetation–environment associations is fundamental in forming proper forest management methods and improving existing classification structures. We believe that this topic is relevant as part of the ongoing search for new solutions within all significant forest ecological classifications.