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Item type:Publication, Shaking up assumptions: Earthquakes have rarely triggered Andean glacier lake outburst floods(John Wiley and Sons Inc, 2024-04-16)As the world’s glaciers recede in response to a warming atmosphere, a change in the magnitude and frequency of related hazards is expected. Among the most destructive hazards are glacier lake outburst floods (GLOFs), and their future evolution is concerning for local populations and sustainable development policy. Central to this is a better understanding of triggers. There is a long-standing assumption that earthquakes are a major GLOF trigger, and seismic activity is consistently included as a key hazard assessment criterion. Here, we provide the first empirical evidence that this assumption is largely incorrect. Focusing on the Tropical Andes, we show that, of 59 earthquakes (1900–2021) the effects of which intersect with known glacier lakes, only one has triggered GLOFs. We argue that, to help develop climate resilient protocols, the focus for future assessments should be on understanding other key GLOF drivers, such as thawing permafrost and underlying structural geology. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Future changes of precipitation types in the Peruvian Andes(Nature Research, 2024-12-01)In high-altitude regions, such as the Peruvian Andes, understanding the transformation of precipitation types under climate change is critical to the sustainability of water resources and the survival of glaciers. In this study, we investigate the distribution and types of precipitation on a tropical glacier in the Peruvian Central Andes. We utilized data from an optical-laser disdrometer and compact weather station installed at 4709 m ASL, combined with future climate scenarios from the CMIP6 project, to model potential future changes in precipitation types. Our findings highlight that increasing temperatures could lead to significant reductions in solid-phase precipitation, including snow, graupel and hail, with implications for the mass balance of Andean glaciers. For instance, a 2 °C rise might result in less than 10% of precipitation as solid, in regard to the present day, transforming the hydrological processes of the region. The two future climate scenarios from the CMIP6 project, SSP2-4.5 and SSP5-8.5, offer a broad perspective on potential climate outcomes that could impact precipitation patterns in the Andes. Our study underscores the need to revisit and expand our understanding of high-altitude precipitation in the face of climate change, paving the way for improved water resource management strategies and sustainable glacier preservation efforts in these fragile ecosystems.1 - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Rethinking Water Sustainability: Precipitation Changes in the Peruvian Andes in the Face of Climate Change(2023-06-13)In high-altitude regions, such as the Peruvian Andes, understanding the transformation of precipitation types under climate change is critical to the sustainability of water resources and the survival of glaciers. Conventional wisdom has primarily focused on the snow-rain dichotomy, often overlooking the potential importance of graupel and hail. In this study, we offer a fresh perspective on this issue, investigating the distribution and types of precipitation on a tropical glacier in the Central Andes. We utilized data from an optical-laser disdrometer and compact weather station installed at 4709 m ASL, combined with future climate scenarios from the CMIP6 project, to model potential future changes in precipitation, including the often-ignored hydrometeor forms of graupel and hail. Our findings highlight that increasing temperatures could lead to significant reductions in solid-phase precipitation, including graupel and hail, with implications for the mass balance of Andean glaciers. For instance, a 2°C rise might result in less than 10\% of precipitation as solid, transforming the hydrological processes of the region. The two future climate scenarios from the CMIP6 project, SSP2-4.5 and SSP5-8.5, offer a broad perspective on potential climate outcomes that could impact precipitation patterns in the Andes. Our study underscores the need to revisit and expand our understanding of high-altitude precipitation in the face of climate change, paving the way for improved water resource management strategies and sustainable glacier preservation efforts in these fragile ecosystems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Generating high-resolution climate data in the Andes using artificial intelligence: A lightweight alternative to the WRF model(Elsevier BV, 2025-12-01)In weather forecasting, generating atmospheric variables for regions with complex topography, such as the Andean regions with peaks reaching 6500 m above sea level, poses significant challenges. Traditional regional climate models often struggle to accurately represent the atmospheric behavior in such areas. Furthermore, the capability to produce high spatio-temporal resolution data (less than 27 km and hourly) is limited to a few institutions globally due to the substantial computational resources required. This study presents the results of atmospheric data generated using a new type of artificial intelligence (AI) models, aimed to reduce the computational cost of generating downscaled climate data using climate regional models like the Weather Research and Forecasting (WRF) model over the Andes. The WRF model was selected for this comparison due to its frequent use in simulating atmospheric variables in the Andes. Our results demonstrate a higher downscaling performance for the four target weather variables studied (temperature, relative humidity, zonal and meridional wind) over coastal, mountain, and jungle regions. Moreover, this AI model offers several advantages, including lower computational costs compared to dynamic models like WRF and continuous improvement potential with additional training data. • We propose an AI model to generate high-resolution climate data. • The model, based on ConvLSTM, predicts temperature, humidity, and wind accurately. • It runs up to 18× faster than WRF with lower memory and storage needs. • It shows promising accuracy across regions with varied topography and climate. • This approach offers an efficient alternative for climate modeling in low-resource areas.1