Efecto del cambio climático en la oferta hídrica superficial de la cuenca del río Ilave - Puno

El incremento de la temperatura global viene alterando el ciclo hidrológico. Los efectos se reflejarán en la variación de los componentes del balance hidrológico. La investigación se limitó en evaluar la oferta hídrica superficial de la cuenca del río Ilave mediante Modelos Climático Globales (GCMs). No obstante, para proyectarse al futuro es necesario entender el presente. Por ello se representó los caudales a escala diaria mediante el modelo hidrológico SAC-SMA en tres periodos de tiempo: histórico (1975 – 2005), presente (1991 – 2016) y futuro (2020 - 2100). El modelo fue calibrado y validado dentro del periodo presente obteniendo valores de Nash-Sutcliffe de 0.86 y 0.82, respectivamente. Para representar el clima futuro se evaluó 21 GCMs de la base de datos NEX-GDDP de la NASA en función de cuan bien representen las estadísticas de precipitación y temperatura actual (1981 – 2005). El modelo CESM1-BGC obtuvo los más altos indicadores en el análisis gráfico y los estadísticos de coeficiente de determinación (R^2) y error cuadrático medio (RMSE). Para corregir los errores sistemáticos del CESM1-BGC se generó funciones de transferencia mensual para cada estación de la cuenca. Se simularon dos Trayectorias Representativas de Concentración (RCP4.5 y RCP8.5) tanto para el futuro cercano (2020-2050) y futuro lejano (2070-2100). La oferta hídrica anual respecto al periodo histórico disminuiría en menos 3% hasta menos 24% a excepción del futuro lejano del RCP 8.5, donde se presentaría un incremento (58%). A nivel mensual, las mayores variaciones se manifestarían en febrero (-8%), marzo (23%), abril (30%) y noviembre (-18%). A nivel de estaciones, tendríamos incremento en otoño (82%) y disminución en invierno (-2%). Finalmente, los hidrogramas mensuales muestra un desplazamiento de los máximos caudales registrados (febrero) hacia el mes de marzo. Para el futuro cercano podemos aseverar un escenario con disminución de caudales, mientras el futuro lejano es aún incierto.Tesi

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO

Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory

The Auger Engineering Radio Array (AERA) is part of the Pierre Auger Observatory and is used to detect the radio emission of cosmic-ray air showers. These observations are compared to the data of the surface detector stations of the Observatory, which provide well-calibrated information on the cosmic-ray energies and arrival directions. The response of the radio stations in the 30 to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of the incoming electric field. For the latter, the energy deposit per area is determined from the radio pulses at each observer position and is interpolated using a two-dimensional function that takes into account signal asymmetries due to interference between the geomagnetic and charge-excess emission components. The spatial integral over the signal distribution gives a direct measurement of the energy transferred from the primary cosmic ray into radio emission in the AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air shower arriving perpendicularly to the geomagnetic field. This radiation energy -- corrected for geometrical effects -- is used as a cosmic-ray energy estimator. Performing an absolute energy calibration against the surface-detector information, we observe that this radio-energy estimator scales quadratically with the cosmic-ray energy as expected for coherent emission. We find an energy resolution of the radio reconstruction of 22% for the data set and 17% for a high-quality subset containing only events with at least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO

Measurement of the Radiation Energy in the Radio Signal of Extensive Air Showers as a Universal Estimator of Cosmic-Ray Energy

We measure the energy emitted by extensive air showers in the form of radio emission in the frequency range from 30 to 80 MHz. Exploiting the accurate energy scale of the Pierre Auger Observatory, we obtain a radiation energy of 15.8 \pm 0.7 (stat) \pm 6.7 (sys) MeV for cosmic rays with an energy of 1 EeV arriving perpendicularly to a geomagnetic field of 0.24 G, scaling quadratically with the cosmic-ray energy. A comparison with predictions from state-of-the-art first-principle calculations shows agreement with our measurement. The radiation energy provides direct access to the calorimetric energy in the electromagnetic cascade of extensive air showers. Comparison with our result thus allows the direct calibration of any cosmic-ray radio detector against the well-established energy scale of the Pierre Auger Observatory.Comment: Replaced with published version. Added journal reference and DOI. Supplemental material in the ancillary file

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio