In order to understand the contribution of the continental crust on arc magmas, we proceeded with a geochemical study of the Pleistocene eruptive products emitted by Sara Sara volcano in Southern Peru. This quaternary stratovolcano, and the surrounding monogenetic cones, are located at the northern arc termination of the Central Volcanic Zone (CVZ) of the Andes, an arc segment constructed on a thick continental crust. Based on major, trace elements and Sr-Nd-Pb isotopic ratios, we demonstrate that the observed geochemical variations are related to an assimilation-fractional crystallisation (AFC) process dominated by plagioclase and amphibole fractionation coupled with assimilation of the upper continental crust. To decipher the magmatic processes located in the lower continental crust, we extrapolated these geochemical trends towards more basic terms using the compositions of surrounding monogenic volcanoes. We achieve this by using trace element ratios vs. SiO₂diagrams to estimate the hypothetical composition of Sara Sara’s parental magmas before their differentiation by fractional crystallisation. These hypothetical compositions are similar to those of the cones in the AndahuaHuambo and Yura monogenic volcanic fields, representing the most primitive lavas in the Peruvian CVZ. By doing so, we demonstrate that the parental andesitic magmas of Sara Sara volcano exhibit a garnet-rutile signature, characterised by HREE depletion, and elevated Dy/Yb, Sr/Y and Nb/Ta ratios. We discuss the origin of this signature and conclude that it is acquired by partial melting of mafic lithologies leaving residues with garnet and rutile at the deep continental crust. These processes formed enriched basaltic melts and rhyo-dacitic melts which were mixed with mantle-derived magma, thereby generating hybrid andesitic parental magmas. In our scenario, arc andesites are generated in two steps including lower continental crust partial melting and subsequent hybridation with mantle-derived magmas, followed by an assimilation-fractional process in the upper crust. One sentence summary: In the context of the termination of a continental volcanic arc, the high-pressure melting process at the base of the continental crust allows the andesites to acquire a garnet-rutile signature, which is progressively modified by assimilation-crystallisation processes during differentiation at lower pressures.

In this paper, we outline the need for a coordinated international effort toward the building of an open-access Global Ocean Oxygen Database and ATlas (GO₂DAT) complying with the FAIR principles (Findable, Accessible, Interoperable, and Reusable). GO₂DAT will combine data from the coastal and open ocean, as measured by the chemical Winkler titration method or by sensors (e.g., optodes, electrodes) from Eulerian and Lagrangian platforms (e.g., ships, moorings, profiling floats, gliders, ships of opportunities, marine mammals, cabled observatories). GO₂DAT will further adopt a community-agreed, fully documented metadata format and a consistent quality control (QC) procedure and quality flagging (QF) system. GO₂DAT will serve to support the development of advanced data analysis and biogeochemical models for improving our mapping, understanding and forecasting capabilities for ocean O₂ changes and deoxygenation trends. It will offer the opportunity to develop quality-controlled data synthesis products with unprecedented spatial (vertical and horizontal) and temporal (sub-seasonal to multi-decadal) resolution. These products will support model assessment, improvement and evaluation as well as the development of climate and ocean health indicators. They will further support the decision-making processes associated with the emerging blue economy, the conservation of marine resources and their associated ecosystem services and the development of management tools required by a diverse community of users (e.g., environmental agencies, aquaculture, and fishing sectors). A better knowledge base of the spatial and temporal variations of marine O₂ will improve our understanding of the ocean O₂ budget, and allow better quantification of the Earth’s carbon and heat budgets. With the ever-increasing need to protect and sustainably manage ocean services, GO₂DAT will allow scientists to fully harness the increasing volumes of O₂ data already delivered by the expanding global ocean observing system and enable smooth incorporation of much higher quantities of data from autonomous platforms in the open ocean and coastal areas into comprehensive data products in the years to come. This paper aims at engaging the community (e.g., scientists, data managers, policy makers, service users) toward the development of GO₂DAT within the framework of the UN Global Ocean Oxygen Decade (GOOD) program recently endorsed by IOC-UNESCO. A roadmap toward GO₂DAT is proposed highlighting the efforts needed (e.g., in terms of human resources).

In ionospheric research, accurately representing the topside electron density profile is essential for predicting radio wave propagation and mitigating ionospheric effects on communication and navigation systems. Despite advances in global models and satellite observations, region-specific and continuous modelling of the topside ionosphere remains challenging due to limited in-situ measurements. Reliable reconstruction of the topside ionosphere using empirical formulations requires precise knowledge of the scale height at the F2 peak (H0) and its linear altitudinal variation (α), especially over the electrodynamically complex equatorial and low-latitude regions. In this study, 13 years (2013-2024) of incoherent scatter radar measurements from the Jicamarca Radio Observatory, Peru, a prominent dip-equatorial station, are utilized to extract realistic topside parameters. A Hybrid reconstruction strategy is employed to adaptively select best suitable formulation between the α-Chapman and semi-Epstein formulations at each time step using fitting error metrics derived from least-square optimization. This approach enables simultaneous and physically consistent estimation of H0 and α.The results revealed pronounced diurnal, seasonal, and solar-geomagnetic variability in the topside parameters. Overall, this analysis demonstrate the effectiveness of Hybrid approach in improving the topside electron density reconstruction, and the insights gained may be integrated with existing models to enhance their regional accuracy.

Malaria imposes a major health burden in the Peruvian Amazon, and its early warning is essential for effective disease prevention. The tropical sea surface temperature (SST) variability, fundamentally shaping the global weather patterns, may also alter malaria transmission and potentially improve its long‐lead predictability. In this study, we propose a machine learning‐based methodology that leverages comprehensive tropical SST variability for malaria prediction in the Peruvian Amazon. First, we demonstrate that significant correlations broadly exist between tropical SST anomalies and Peruvian malaria occurrence across different seasons and time lags, confirming the potential predictability from the tropical ocean. Then, we apply the self‐ organizing map to synthesize the spatiotemporally varying SST‐malaria relationship and identify a unique dynamic SST index for Peruvian malaria. The dynamic SST index provides better performance (higher correlation coefficients and lower root mean square errors) in the generalized linear model, compared to the traditional El Niño–Southern Oscillation (ENSO) index, with lead times exceeding 3 months. Furthermore, the dynamic SST index captures the evolution of the ENSO life cycle from its precursor climate mode (Pacific Meridional Mode) and appears to influence Peruvian malaria by altering the local near‐surface air temperature and specific humidity. Such underlying mechanisms provide the physically plausible basis for the long‐lead predictability of Peruvian malaria using a machine learning‐based remote predictor. Last but not least, we provide open‐source code for broad applications in linking tropical SST variability and vector‐borne disease transmission, or other climate‐sensitive socioeconomic issues.

Water Stable Isotopes (δ18O, δ2H) are valuable tools for tracing sources and interactions in the water cycle, providing important information dedicated to understanding physical mechanisms related to global climate. Despite their significance, the topic of isotopic research in South America has been hindered by limited data. To address this gap, we launched a national-level water stable isotope dataset covering different water sources in Peru (WSI-PeruDB). The dataset contains curated in-house data and incorporates previously published records from various locations collected between 2000 and 2021. The WSIPeruDB dataset is composed of 489 water collection sites and allows a comprehensive use of the dataset by implementing standardized metadata templates containing essential geographical information such as latitude, longitude, and altitude (from sea level to 5000 m a.s.l), and sampling information such as sample type (e.g. groundwater, precipitation, river, spring, and others) and sampling frequency (e.g. biweekly, daily, monthly). The WSIPeruDB dataset is publicly available on Zenodo, facilitating access and use for the scientific community.

We present a new empirical vertical E×B drift model developed using ground-based magnetometer, radar, and satellite data over equatorial latitude regions. We first implement an algorithm relating magnetometer derived equatorial electrojet (EEJ) and vertical ion plasma drift (equivalent to vertical E×B drift within magnetic latitudes of ±5° and altitudes of about 400–550 km) from the Communications and Navigation Outage Forecasting System (C/NOFS) satellite at different longitude sectors. The relationship between EEJ and C/NOFS vertical E×B drift is developed separately at different longitudes over the globe at coincidental times when both data sets are available. These relationships are then used to estimate continuous vertical E×B drift at each epoch of EEJ observation over the respective longitude sectors during local daytime. The reconstructed vertical E×B drift data are combined with global C/NOFS vertical E×B drifts and JULIA data set to develop a global vertical E×B drift model. Validation using Ion Velocity Meter (IVM) drifts from ICON satellite for January to August 2022 shows that our model improves vertical E×B drift global modeling by over 20% compared to the current climatology representation.

The Central Volcanic Zone of the Andes (CVZA) has been the focus of volcanological research for decades, becoming a very important site to understand a number of volcanic processes. Despite most of the research in the CVZA being carried out by foreign scientists, the last two decades have seen a significant increase in contributions by regional researchers. This surge has been facilitated by the creation of new volcanic observatories, improvement of the monitoring networks, creation of postgraduate programs where new local volcanologists are trained, creation of specialized research nuclei or groups, and increasing investment in research. This article presents a review of the evolution of the contributions of the regional volcanological community to the knowledge of the CVZA in the last 20 years (2000–2019), both from research and monitoring institutions in Peru, Bolivia, Argentina, and Chile. Based on updates made by the regional groups, a new list of active/potentially active volcanoes of the CVZA is presented, as is a complete database for article published on the CVZA. We find that a significant motivator has been regional volcanic unrest that has triggered new investment. Perú is the country with the highest investment in monitoring and research and is the best instrumented, Argentina is the country with the highest number of local participation in published papers in the domain of volcanology and magmatic systems, and Chilean volcanoes are the focus of the highest number of articles published. The current situation and general projections for the next decade (2020–2030) are also presented for each country, where we believe that the over the next 10 years, will be increased the monitoring and research capabilities, improved the scientific knowledge with more participation of regional institutions, and strengthen the collaboration and integrated work between CVZA countries, especially in border volcanoes.

To better understand the recent internal structure of Misti volcano, we determined a 3D S-wave velocity model applying Ambient Noise Tomography (ANT). We used data from 23 broadband and short-period seismic stations temporarily installed at Misti volcano between March and December 2011. This dataset allowed us to obtain empirical Green's functions by cross-correlating seismic ambient noise signals. Then, we retrieved 104 dispersion curves using the frequency-time analysis (FTAN) and, through a non-linear multiscale inversion, we obtained nine 2-D Rayleigh waves group velocity maps for periods in the range 0.7 s - 2 s. Finally, we carried out the depth inversion through a Bayesian transdimensional inversion to obtain a 3-D S-wave velocity model down to 3 km depth. Our study highlights five relevant seismic velocity anomalies. We observed the presence of three high-velocity zones located in the west-northwest, southwest and southeast parts of the crater, that could be related to intrusive bodies possibly associated with the formation of Misti volcano. We also observed two low-velocity anomalies in the volcano's western and central parts, which coincide with previous studies' findings and are related to fractured and weakened materials associated with the external caldera collapse and recent eruption episodes.

The grasslands of the southern Andes are critical ecosystems for the rural population, but they have been significantly affected by fires. While fire ignitions are anthropogenic, their occurrence and spread are shaped by climatic, vegetational, and topographic factors. This study identified the main environmental and human drivers of fire occur rence and extent in high Andean grasslands. We developed generalized linear models with 14 and 22 variables for the fire occurrence and extent model, respectively. Various metrics (e.g. AIC, AUC, pseudo-R²) were applied to validate the best-performing model and assess its performance. Our findings suggest that elevation, maximum temperature, soil adjusted vegetation index, and topographic position index are the primary drivers of fire occurrence. For fire extent, grass cover, elevation, topographic position index, and rock cover were the most influential factors. The models explained 21% and 60% of the variability in fire occurrence and extent, respectively. This study identifies key environmental factors influencing fire occurrence and extent, providing valuable insights for improving fire management strategies, particularly in fire-prone ecosystems such as grasslands. Since the temperature was a contributing factor to fire occurrence, this highlights the importance of prevention and reduction strategies in the context of climate change.

Equatorial spread-F (ESF) is an irregularity caused by plasma instabilities on the night side that causes signal degradation and disruptions to the GNSS signals. Ionosondes could detect ESF as it appears as a diffused echo in the ionogram images. This study proposes a Convolutional Neural Network (CNN) model that can automatically detect ESF within the ionogram images and classify its type. The model has been trained using 2646 manually labeled ionograms from the Low Latitude Ionospheric Sensor Network (LISN) VIPIR Ionosondes in South America. The data used to train the model was measured from 2019 to 2024. The model was able to classify the testing images into six categories: Clear class, frequency spread-F (FSF), range spread-F (RSF), mixed spread-F (MSF), strong spread-F (SSF), and Unidentified class. It demonstrated high classification accuracy within the extracted test subset and a further random test, showcasing robustness and consistency in detection accuracy across all classes. Furthermore, the model performance has been evaluated and compared with other baseline models: VGG16, VGG19, ResNet18, and Inception-V3 in the same environment. Additionally, a comparison with published models is provided. Our model showed a higher consistency in classification accuracy across all classes compared to the mentioned models.

This article introduces a framework to supplement short historical catalogs with synthetic catalogs and determine large earthquakes’ recurrence. For this assessment, we developed a parameter estimation technique for a probabilistic earthquake occurrence model that captures time and space interactions between large mainshocks. The technique is based on a two‐step Bayesian update that uses a synthetic catalog from physics‐based simulations for initial parameter estimation and then the historical catalog for further calibration, fully characterizing parameter uncertainty. The article also provides a formulation to combine multiple synthetic catalogs according to their likelihood of representing empirical earthquake stress drops and Global Positioning System‐inferred interseismic coupling. We applied this technique to analyze large‐magnitude earthquakes’ recurrence along 650 km of the subduction fault’s interface located offshore Lima, Peru. We built nine 2000 yr long synthetic catalogs using quasi‐dynamic earthquake cycle simulations based on the rate‐and‐state friction law to supplement the 450 yr long historical catalog. When the synthetic catalogs are combined with the historical catalog without propagating their uncertainty, we found average relative reductions larger than 90% in the recurrence parameters’ uncertainty. When we propagated the physics‐based simulations’ uncertainty to the posterior, the reductions in uncertainty decreased to 60%–70%. In two Bayesian assessments, we then show that using synthetic catalogs results in higher parameter uncertainty reductions than using only the historical catalog (69% vs. 60% and 83% vs. 80%), demonstrating that synthetic catalogs can be effectively combined with historical data, especially in tectonic regions with short historical catalogs. Finally, we show the implications of these results for time‐dependent seismic hazard.

El cambio climático está incrementando la frecuencia e intensidad del calor extremo a nivel global, con impactos severos en la salud humana. En Perú, aunque históricamente no se han registrado olas de calor comparables a las de otras regiones, existen reportes de muertes por calor durante los eventos de El Niño en la costa norte. Este estudio estima los efectos del cambio climático y El Niño en la ocurrencia de calor peligroso y mortal en Perú, mediante el empleo del heat index como indicador y los datos climáticos observados y proyectados al 2050. Los resultados muestran que, bajo escenarios de cambio climático, la Amazonía experimentaría calor peligroso y mortal de manera casi permanente. Mientras tanto, en la costa norte y central, las condiciones de calor extremo, que actualmente se presentan durante eventos de El Niño, se exacerbarán fuertemente con el cambio climático, lo cual incrementará los riesgos para la salud pública. Dado el rápido aumento del peligro asociado al calor extremo, es urgente desarrollar estrategias de adaptación para reducir el impacto del peligro rápidamente emergente del calor extremo en la población peruana.

El presente avance de investigación analiza los datos de temperatura y salinidad recolectados entre el 4 y 9 de febrero de 2020 durante la vigesimoséptima campaña científica del Perú a la Antártida (ANTAR XXVII), a bordo del buque oceanográfico con capacidad polar BAP Carrasco (BOP-171). Los datos analizados corresponden a la región del estrecho de Bransfield (EB), ubicado entre el suroeste de la isla Rey Jorge y la península antártica, recolectados mediante los instrumentos CTDO (ConductivityTemperature-Depth-Oxygen) y uCTD (Underway Conductivity-Temperature-Depth), y procesados con el software Ocean Data View. El análisis preliminar muestra que, en comparación con registros de 2002, la temperatura y la salinidad mantuvieron un patrón de distribución similar en la región. Sin embargo, hay una leve profundización de las isolíneas que amerita mayor análisis.

Atmospheric aerosols are known to alter the Earth’s radiative balance and influence climate. However, accurately quantifying the magnitude of aerosol-induced radiative forcing remains challenging. We characterize optical properties of biomass-burning (BB) and non-biomass-burning (NB) aerosols and quantify BB aerosol radiative forcing at two AERONET (AErosol RObotic NETwork) sites in Huancayo (Peru) and La Paz (Bolivia) during 2015–2021. From AERONET data, we derive aerosol optical depth (AOD), Ångström exponent (AE), single-scattering albedo (SSA), and asymmetry parameter (ASY). We then employ the SBDART model to calculate aerosol radiative forcing (ARF) on monthly and multiannual timescales. BB aerosols peak in September (AOD: 0.230 at Huancayo; 0.235 at La Paz), while NB aerosols reach maxima in September at Huancayo (0.109) and November at La Paz (0.104). AE values exceeding unity for BB aerosols indicate fine-mode dominance. Huancayo exhibited the highest BB ARF in November: +16.4 W m−2 at the top of the atmosphere (TOA), –18.6 W m−2 at the surface (BOA), and +35.1 W m−2 within the atmospheric column (ATM). This was driven by elevated AOD and high scattering efficiency. At La Paz, where SSA data was only available for September, BBARF values were also significant (+15.16 at TOA, –17.52 at BOA, and +32.73 W m−2 within the ATM). This result underscores the importance of quantifying the ARF, particularly over South America where data is scarce.

The world's eastern boundary upwelling systems (EBUSs) contribute disproportionately to global ocean productivity and provide critical ecosystem services to human society. The impact of climate change on EBUSs and the ecosystems they support is thus a subject of considerable interest. Here, we review hypotheses of climate-driven change in the physics, biogeochemistry, and ecology of EBUSs; describe observed changes over recent decades; and present projected changes over the twenty-first century. Similarities in historical and projected change among EBUSs include a trend toward upwelling intensification in poleward regions, mitigatedwarming in near-coastal regions where upwelling intensifies, and enhanced water-column stratification and a shoaling mixed layer. However, there remains significant uncertainty in how EBUSs will evolve with climate change, particularly in how the sometimes competing changes in upwelling intensity, source-water chemistry, and stratification will affect productivity and ecosystem structure. We summarize the commonalities and differences in historical and projected change in EBUSs and conclude with an assessment of key remaining uncertainties and questions. Future studies will need to address these questions to better understand, project, and adapt to climate-driven changes in EBUSs.

This work shows a 3-year climatology of the horizontal components of the solar diurnal tide, obtained from wind measurements made by a multistatic specular meteor radar (SIMONe) located in Jicamarca, Peru (12°S, 77°W). Our observations show that the meridional component is more intense than the zonal component, and that it exhibits its maxima shifted with respect to the equinox times (i.e., the largest peak occurs in August–September, and the second one in April–May). The zonal component only shows a clear maximum in August–September. This observational climatology is compared to a climatology obtained with the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X). Average comparisons indicate that the model amplitudes are 50% smaller than the observed ones. The WACCM-X results are also used in combination with observed altitude profiles of the tidal phases to understand the relative contributions of migrating and non-migrating components. Based on this, we infer that the migrating diurnal tide (DW1) dominates in general, but that from June until September (November until July) the DE3 (DW2) may have a significant contribution to the zonal (meridional) component. Finally, applying wavelet analysis to the complex amplitude of the total diurnal tide, modulating periods between 5 and 80 days are observed in the SIMONe measurements and the WACCM-X model. These modulations might be associated to planetary waves and intraseasonal oscillations in the lower tropical atmosphere.

Multiple linear regression models were developed for 1–3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, on the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service—SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000–13 period, and verified their skill for the 2014–19 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff). The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root-mean-square error (RMSE) for the 1-day lead forecasts were 0.37°–0.53° and 0.74°–1.76°C for the empirical model, respectively, but from around −0.05° to 0.24° and 0.88°–4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June–August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality. There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the low-level cyclonic vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts.

Eutrophication and global warming are key drivers of oxygen loss, also termed deoxygenation, in coastal ecosystems worldwide. Artificial reoxygenation has been suggested as a local or regional solution to increase oxygen concentrations and improve water quality by various parties, including water managers and industry. Three main approaches have been proposed: (1) bubbling with air with the aim to destratify and mix the water column (2) injection of pure oxygen and (3) pumping of oxygenated water to greater water depths (downwelling). In this review, we summarize the results of recent field trials and other implementations of artificial reoxygenation in coastal systems, which, to date, only involve small bays and estuaries. We also discuss potential benefits and risks. While the recent trials indicate that reoxygenation of the water column can be achieved, low oxygen conditions returned rapidly within days to months of discontinuing operations. This illustrates that artificial reoxygenation typically only provides a temporary solution to deoxygenation. Potential side effects of artificial reoxygenation could include enhanced emissions of the greenhouse gas carbon dioxide and, upon bubbling and destratification in shallow waters, also of methane. Additionally, downwelling could lead to warming and an associated increased oxygen demand near the seafloor. Reoxygenation will not necessarily reduce the nutrient availability for phytoplankton, implying that water quality may remain poor. We recommend a careful, case-by-case assessment of the suitability of artificial reoxygenation in coastal systems prior to implementation and monitoring before, during and after each intervention. Any field trials should involve all relevant parties, including scientists and local communities, and results should be reported with full transparency. While in the short-term, artificial reoxygenation may be useful to alleviate oxygen loss in some coastal systems, long-term improvements in the oxygen levels and quality of coastal waters require reductions in nutrient inputs and greenhouse gas emissions.

The decadal variability in summer precipitation over the Central Andes (10°–30°S) is investigated from 1921 to 2010 using low-pass filtered time series of the central and eastern El Niño–Southern Oscillation (ENSO) Pacific (C and E) indices, the South Pacific Convergence Zone (SPCZ) index, the Atlantic SST indices, Atlantic Multidecadal Oscillation (AMO) index, North Atlantic Oscillation (NAO) index, and ERA-20C reanalysis. Additionally, an empirical-statistical downscaling (ESD) model was built. A rotated empirical orthogonal function (REOF) analysis shows that the first leading mode of precipitation (RPC1) represents 38.2% of the total decadal variance. RPC2, RCP3, and RPC4 represent 18.8, 12.8, and 9.7% of the total decadal variance, respectively. Furthermore, RPC1 features highest loadings over most of the region. RPC2 features a dipole of highest loadings over the southernmost Bolivian Altiplano and lowest loadings over the northwestern Argentinian Andes. Conversely, RPC3 presents highest loadings over the eastern-central Bolivian Altiplano and northwestern Argentinian Andes. RPC4 features highest loadings over the southern Bolivian Andes. RPC1 and RPC3 wet summers are associated with moisture transport from the Amazon basin, but RPC1 features the strengthening upper-level Bolivian high-Nordeste low system over South America. Conversely, RPC2 and RPC4 wet summers are associated with local processes induced by southward displacement of the South Atlantic Convergence Zone and warm sea surface temperature (SST) anomalies over the Indian Ocean, respectively. According to the ESD model, the decadal variability in the central and eastern Pacific (CP and EP) and Atlantic Ocean reproduces the decadal component of the DJF precipitation over most of the Central Andes.

In recent decades, the city of Arequipa, Peru, has experienced rapid and largely unplanned urban growth, increasing its exposure to lahars (debris and hyperconcentrated flows) triggered by intense rainfall during the rainy season (December - March). These flows descend through ravine channels from the Misti volcano, causing significant damage and loss of life in suburban areas and the historic center of Arequipa. This context underscores the urgent need for an efficient, real-time, and continuous lahar monitoring system. Here we present the development and implementation of an Automatic Lahars Monitoring System (ALMS) deployed across six ravines that extend from the SW flank of Misti volcano and cross the city of Arequipa. The ALMS is an improved version of the Huaicos Monitoring System (SMH), which has operated since 1990 in the Quebrada Rioseco near Lima, Perú. The ALMS comprises ten autonomous, solar-powered remote stations equipped with radar motion sensors, LiDAR level sensors, surveillance cameras, and machine-learning-based image analysis for continuous monitoring and detection of lahar activity. During the 2024–2025 rainy season, the ALMS successfully detected two lahar events in the Quebrada El Pato and Quebrada Venezuela channels, demonstrating its operational reliability and effectiveness as an early-warning tool. Owing to its low cost, modular design, and robust performance, the ALMS represents a scalable solution that can be replicated in other volcanic and non-volcanic regions to reduce the impacts of debris flows and enhance disaster risk management.