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The mountain cryosphere is one of the strongest affected systems by climate change. Glacier shrinkage leads to cascading impacts, including changes in river flow regimes, availability of water resources for downstream populations and economy, changes in the occurrence and severity of natural hazards, and cultural changes associated with landscape character and identity. In this study, we analyze impacts of mountain cryosphere change through a lens of Loss and Damage (L&D), a mechanism of international climate policy that tries to evaluate and reduce negative consequences of climate change for societies. We analyze the effects of climate change on glacier change, glacier lake formation and growth, hydrological regimes, and associated impacts on human societies in the Cordillera Blanca in the Peruvian Andes, now and under future scenarios. We use various methods such as literature review, glacial lake outburst flood, and hydrologic modeling to examine three major dimensions of cryospheric change and associated human impacts: (i) ice loss; (ii) glacial hazards; and (iii) variability of water availability. We identify the damage and losses in terms of the number of people affected by glacial hazards, monetized agricultural crop loss due to water loss, and non-economic values local people attribute to glacier loss. We find that different levels of warming have important negative but differentiated effects on natural and human systems. We also contend that the extent of loss and damage will largely be determined by governance and adaptation decisions such as water resource management and disaster risk management. We suggest that these lines of evidence are more explicitly taken into account in L&D policies.

In Latin America, there has been tremendous progress towards eliminating canine rabies. Major components of rabies elimination programs leading to these successes have been constant and regular surveillance for rabid dogs and uninterrupted yearly mass dog vaccination campaigns. Unfortunately, vital measures to control COVID-19 have had the negative trade-off of jeopardizing these rabies elimination and prevention activities. We aimed to assess the effect of interrupting canine rabies surveillance and mass dog vaccination campaigns on rabies trends. We built a deterministic compartment model of dog rabies dynamics to create a conceptual framework for how different disruptions may affect rabies virus transmission. We parameterized the model for conditions found in Arequipa, Peru, a city with active rabies virus transmission. We examined our results over a range of plausible values for R 0 (1.36–2.0). Also, we prospectively evaluated surveillance data during the pandemic to detect temporal changes. Our model suggests that a decrease in canine vaccination coverage as well as decreased surveillance could lead to a sharp rise in canine rabies within months. These results were consistent over all plausible values of R 0 . Surveillance data from late 2020 and early 2021 confirms that in Arequipa, Peru, rabies cases are on an increasing trajectory. The rising rabies trends in Arequipa, if indicative to the region as whole, suggest that the achievements made in Latin America towards the elimination of dog-mediated human rabies may be in jeopardy.

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were prepared using remotely sensed and auxiliary datasets. The LIFs were evaluated using multi-collinearity statistics and their relative importance was measured to select the most discriminative LIFs using the ensemble feature selection method, which was developed using Chi-square, gain ratio, and relief-F methods. We evaluated the performance of ten different ML algorithms (linear discriminant analysis, mixture discriminant analysis, bagged cart, boosted logistic regression, k-nearest neighbors, artificial neural network, support vector machine, random forest, rotation forest, and C5.0) using different accuracy statistics (sensitivity, specificity, area under curve (AUC), and overall accuracy (OA)). We used suitable combinations of individual ML models to develop different ensemble ML models and evaluated their performance in LSM. We assessed the impact of LIFs on ML performance. Among all individual ML models, the k-nearest neighbors (sensitivity = 0.72, specificity = 0.82, AUC = 0.86, OA = 78%) and artificial neural network (sensitivity = 0.71, specificity = 0.85, AUC = 0.87, OA = 79%) algorithms showed the best performance using the top five LIFs, while random forest, rotation forest, and C5.0 (sensitivity = 0.76–0.81, specificity = 0.87, AUC = 0.90–0.93, OA = 82–84%) outperformed other models when developed using all twenty-four LIFs. Among ensemble models, the ensemble of k-nearest neighbors and rotation forest, k-nearest neighbors and artificial neural network, and artificial neural network and rotation forest outperformed other models (sensitivity = 0.72–0.73, specificity = 0.83–0.84, AUC = 0.86, OA = 79%) using the top five LIFs. The landslide susceptibility maps derived using these models indicate that ~2–3% and ~10–12% of the total study area fall within the “very high” and “high” susceptibility. The obtained susceptibility maps can be efficiently used to prioritize landslide mitigation activities.

Climate-related phenomena in Peru have been slowly but continuously changing in recent years beyond historical variability. These include sea surface temperature increases, irregular precipitation patterns and reduction of glacier-covered areas. In addition, climate scenarios show amplification in rainfall variability related to the warmer conditions associated with El Niño events. Extreme weather can affect human health, increase shocks and stresses to the health systems, and cause large economic losses. In this article, we study the characteristics of El Niño events in Peru, its health and economic impacts and we discuss government preparedness for this kind of event, identify gaps in response, and provide evidence to inform adequate planning for future events and mitigating impacts on highly vulnerable regions and populations. This is the first case study to review the impact of a Coastal El Niño event on Peru’s economy, public health, and governance. The 2017 event was the third strongest El Niño event according to literature, in terms of precipitation and river flooding and caused important economic losses and health impacts. At a national level, these findings expose a need for careful consideration of the potential limitations of policies linked to disaster prevention and preparedness when dealing with El Niño events. El Niño-related policies should be based on local-level risk analysis and efficient preparedness measures in the face of emergencies.

Introduction Asthma is the most common chronic disease among children worldwide, with 80% of asthma-related deaths occurring in low- and middle-income countries (LMICs). While evidence-based guidelines exist for asthma treatment and management, adoption of guideline-based practices is low in high-income country and LMIC settings alike. While asthma prevalence among children and adolescents in Lima, Peru is in the range of 13%–19.6%, our data suggest that < 5% of children in low-resource communities are currently taking guideline-based therapies. There is an urgent need for effective, locally tailored solutions to address the asthma treatment gap in low-income communities in Peru. Methods This study aims to develop and test a locally adapted intervention package to improve adoption of self-management practices and utilization of preventive health services for asthma among children in Lima Norte. The intervention package was designed using a systematic, theory-based framework (Capability, Opportunity, Motivation – Behavior Framework) and is rooted in a multi-phased formative research approach. The main study design is an individually randomized implementation-effectiveness hybrid trial enrolling 110 children aged 5–17 years with asthma and their caregivers. Families allocated to the treatment group receive the supported self-management intervention package, while families allocated to the control group receive the standard of care plus asthma education. We will follow participants monthly for six months and evaluate asthma control (Asthma Control Test), healthcare utilization, and medication adherence (Adherence to Refills and Medications Scale). Disease-specific quality of life for children (Pediatric Asthma Quality of Life Questionnaire) and caregivers (Pediatric Asthma Caregiver’s Quality of Life Questionnaire) will be evaluated at baseline, 3 months, and 6 months. We will also evaluate acceptability, feasibility, and fidelity of the intervention using mixed methods approaches. Discussion The long-term goal of this study is to disseminate locally appropriate asthma management strategies in LMIC settings. This study will contribute to the body of knowledge surrounding approaches for developing and evaluating intervention strategies for asthma using systematic, theory-based approaches grounded in local context. Such strategies have the potential to inform the development and adaptation of appropriate and scalable solutions for asthma management in LMIC settings. Trial registration ClinicalTrials.gov, NCT03986177 . Registered on 14 June 2019.

Atmospheric correction is essential in remote sensing, as it reduces the effects of light absorption and scattering by suspended particles and gases, enabling accurate surface reflectance computation from the observed Top-of-Atmosphere (TOA) reflectance. Each satellite sensor requires a customized atmospheric correction processor due to its unique system characteristics. Currently, PeruSAT-1, the first Peruvian remote sensing satellite, does not include this capability in its image processing pipeline, which poses challenges for its effectiveness in defense, security, and natural disaster management applications. This research investigated pixel-based machine learning methods for atmospheric correction of PeruSAT-1, using Sentinel-2 harmonized Bottom-of-Atmosphere (BOA) surface reflectance images as a benchmark, alongside additional atmospheric, terrain, and acquisition parameters. A robust dataset was developed to align data across temporal, spatial, geometric, and contextual conditions. Experimental results showed R2 values between 0.886 and 0.938, and RMSE values ranging from 0.009 to 0.025 compared to the benchmarks. Among the models tested, the Feedforward Neural Network (FFNN) using the Leaky ReLU activation function achieved the best overall performance. These findings confirm the robustness of this approach, offering a scalable methodology for satellites with similar characteristics and establishing a foundation for a customized atmospheric correction pipeline for PeruSAT-1. Future work will focus on diversifying the dataset across spectral and seasonal conditions and optimizing the modeling to address challenges in shorter wavelengths and high-reflectance surfaces.

Glacier hazards threaten societies in mountain regions worldwide. Glacial lake outburst floods (GLOFs) pose risks to exposed and vulnerable populations and can be linked in part to long-term post-Little Ice Age climate change because precariously dammed glacial lakes sometimes formed as glaciers generally retreated after the mid-1800s. This paper provides an interdisciplinary and historical analysis of 40 years of glacier hazard management on Mount Hualcán, at glacial Lake 513, and in the city of Carhuaz in Peru’s Cordillera Blanca mountain range. The case study examines attempted hazard zoning, glacial lake evolution and monitoring, and emergency engineering projects to drain Lake 513. It also analyzes the 11 April 2010 Hualcán rock-ice avalanche that triggered a Lake 513 GLOF; we offer both a scientific assessment of the possible role of temperature on slope stability and a GIS spatial analysis of human impacts. Qualitative historical analysis of glacier hazard management since 1970 allows us to identify and explain why certain actions and policies to reduce risk were implemented or omitted. We extrapolate these case-specific variables to generate a broader socio-environmental framework identifying factors that can facilitate or impede disaster risk reduction and climate change adaptation. Facilitating factors are technical capacity, disaster events with visible hazards, institutional support, committed individuals, and international involvement. Impediments include divergent risk perceptions, imposed government policies, institutional instability, knowledge disparities, and invisible hazards. This framework emerges from an empirical analysis of a coupled social-ecological system and offers a holistic approach for integrating disaster risk reduction and climate change adaptation.

Given the contemporary increase in the frequency, intensity, and duration of extreme anomalous hydromet hazards (droughts, floods, tropical storms, heatwaves), heightened attention of governments, scientists, media, and humanitarian organizations is being given to hydromet early warning systems. The focus of this article is multidisciplinary and multifaceted: it involves connecting an earliest warning indicator associated with the Oceanic Niño Index, one that complements the existing National Oceanic and Atmospheric Administration indicator, with early warning early action and anticipatory action approaches for disaster risk reduction (DRR). This new indicator in theory at least could increase the lead time between the release of an official forecast of an El Niño and the first appearance of its adverse impacts, thereby serving as the earliest warning of an event. As such, this DRR research links new usable earliest warning information, providing additional time to initiate tactical actions to cope with El Niño-spawned hydromet hazards. Integrating an earliest indicator of the likely onset of an El Niño into early action frameworks would hasten humanitarian assistance by providing at-risk communities and humanitarian organizations with more time to consider a range of options for responding to El Niño’s impacts.

Urban growth and natural hazard events are continuous trends and reliable monitoring is demanded by organisations such as the Intergovernmental Panel on Climate Change, the United Nations Office for Disaster Risk Reduction, or the United Nations Human Settlements Programme. CORONA is the program name of photoreconnaissance satellite imagery available from 1960 to 1984 provides an extension of monitoring ranges in comparison to later satellite data such as Landsat that are more widely used. Providing visual comparisons with aerial or high-resolution OrbView satellite imagery, this article demonstrates applications of CORONA images for change detection of urban growth and sprawl and natural hazard exposure. Cases from El Alto/ La Paz in Bolivia, Santiago de Chile, Yungay in Peru, Qazvin in Iran, and Mount St. Helens in the USA are analysed. After a preassessment of over 20 disaster events, the 1970 Yungay earthquake-triggered debris avalanche and the natural hazard processes of the 1980 Mt St. Helens volcanic eruption are further analysed. Usability and limitations of CORONA data are analysed, including the availability of data depending on flight missions, cloud cover, spatial and temporal resolution, but also rather scarce documentation of natural hazards in the 1960s and 70s. Results include the identification of urban borders expanding into hazard-prone areas such as mountains, riverbeds or erosion channels. These are important areas for future research, making more usage of this valuable but little-used data source. The article addresses geographers, spatial planners, political decision makers and other scientific areas dealing with remote sensing.