Assessment of climate Change impact on water supply in Peru

In this work, the simulation of the water supply has been carried out using the SWAT hydrological model to generate streamflows throughout Peru. For this purpose, 35 hydrological stations distributed in the 3 drainages of Peru have been used, simulating the entire country for the first time considering 4,355 sub-basins and 168 hydrographic units (HU); obtaining time series of streamflows for the period 1981-2016. To evaluate the impact of climate change on water supply of Peru, three regional climate models based on dynamic regionalization have been used, obtaining time series from 1981 to 2065 with a spatial resolution of ~ 10 km. Based on these data, and using the delta change method, the streamflows generated in the period 2035-2065 versus 1981-2016 have been compared

Climate Change Impact on Peruvian Biomes

The biodiversity present in Peru will be affected by climatic and anthropogenic changes; therefore, understanding these changes will help generate biodiversity conservation policies. This study analyzes the potential distributions of biomes (B) in Peru under the effects of climate change. The evaluation was carried out using the random forest (RF) method, six bioclimatic variables, and digital topography for the classification of current B in Peru. Subsequently, the calibrated RF model was assimilated to three downscaled regional climate models to project future B distributions for the 2035–2065 horizon. We evaluated possible changes in extension and elevation as well as most susceptible B. Our projections show that future scenarios agreed that 82% of current B coverage will remain stable. Approximately 6% of the study area will change its current conditions to conditions of higher humidity; 4.5% will maintain a stable physiognomy, but with an increase in humidity; and finally, 6% will experience a decrease in humidity but maintain its appearance. Additionally, glaciers and swamps are indicated as the most vulnerable B, with probable losses greater than 50% of their current area. These results demonstrate the need to generate public policies for the adaptation and mitigation of climate effects on B at a national scal

Análisis regional de frecuencia para determinación de mapas de eventos de sequía en Perú

Se aplicó el análisis regional de frecuencia basado en L momentos y el procedimiento de índice de avenida, este enfoque metodológico permite incorporar datos provenientes de estaciones dentro de una región homogénea para mejorar la precisión en las estimaciones de la relación probabilidad cuantil en todos los sitios, de esta manera permite solucionar la escasez de datos en el tiempo con el aumento de datos en el espacio; para la determinación de mapas de periodos de retorno de sequias meteorológicas teóricas correspondientes a 0.2, 0.4 y 0.6 de la precipitación media anual a escala de todo el Perú. Entre los resultados, se identificó 32 regiones homogéneas de lluvia: 16 en la Región Hidrográfica del Pacífico (RHP), 1 en la Región Hidrográfica del Titicaca (RHT) y 15 en la Región Hidrográfica del Amazonas (RHA); utilizando una combinación del análisis de clúster y el enfoque L momentos, sobre la base de 254 estaciones con récords históricos entre 1964 – 2020 provenientes de la red de observación del SENAMHI, para el análisis regional de frecuencia se seleccionó la distribución logística generalizada por su mejor ajuste en 20/32 regiones utilizando el estadístico ZDIST, el cual permitió determinar la curva de crecimiento regional o cuantiles para el país. Finalmente, se obtuvo ecuaciones exponenciales predictoras a escala regional para relacionar los L momentos y la precipitación media anual que permitió generar los mapas de recurrencia de sequias meteorológicas. Se concluye que las zonas con régimen de precipitación más húmedo (mayor precipitación media anual, PMA) se asocian con menor frecuencia de déficit de precipitación respecto de la condición normal, y corresponden a L-Cv de valores menores (RHA) sin embargo en zonas con régimen de precipitación más seco (menor PMA) se asocia con mayor frecuencia de déficit de precipitación y corresponden a L-Cv de valores mayores (RHP). Asimismo, las zonas potenciales a presentar sequias meteorológicas se localizan hacia la RHP y RHT con intervalos de ocurrencia de 3 a 20 años y 5 a 200 años respectivamente, mientras que en la RHA se presentarían con intervalos entre 10 a más de 200 años; por tanto, la mayor frecuencia de sequía se esperaría hacia las zonas baja y media de la RHP (cuencas Chira hasta la Concordia, con mayor extensión hacia el extremo sur del territorio peruano) principalmente por sus características semi áridas a hiperáridas

Hydrological Response Assessment of Land Cover Change in a Peruvian Amazonian Basin Impacted by Deforestation Using the SWAT Model

The watershed hydrologic conditions in the Madre de Dios (MDD) Basin in the Peruvian Amazon have been irreversibly impacted by deforestation and changes in land cover. These changes have also had detrimental effects on the geomorphology, water quality, and aquatic habitat within the basin. However, there is a scarcity of hydrological modeling studies in this area, primarily due to the limited availability of hydrometeorological data. The primary objective of this study was to examine how deforestation impacts the hydrological conditions in the MDD Basin. By implementing the Soil and Water Assessment Tool (SWAT) model, this study determined that replacing 12% of the evergreen broadleaf forest area with bare land resulted in a significant increase in surface runoff, by 38% monthly, a 1% annual reduction of evapotranspiration, and an average monthly streamflow increase of 12%. Changes in spatial patterns reveal that the primary impacted watershed is the Inambari River subbasin, a significant tributary of the Madre de Dios River. This area experiences an annual average surge of 187% in surface runoff generation while witnessing an annual average reduction of 8% in evapotranspiration. These findings have important implications, as they can contribute to instances of flooding and extreme inundation events, which have already occurred in the MDD region

Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data

The objective of this work was to generate and evaluate regional rainfall thresholds obtained from a combination of high-resolution gridded precipitation data (PISCOpd_Op), developed by the National Service of Meteorology and Hydrology of Peru (SENAMHI), and information from observed shallow landslide events. The landslide data were associated with rainfall data, determining triggering and non-triggering rainfall events with rainfall properties from which rainfall thresholds were determined. The validation of the performance of the thresholds was carried out with events that occurred during 2020 and focused on evaluating the operability of these thresholds in landslide warning systems in Peru. Thresholds were determined for 11 rainfall regions. The method of determining the thresholds was based on an empirical–statistical approach, and the predictive performance of the thresholds was evaluated from the “true skill statistics” (TSS) and the area under the curve (AUC). The best predictive performance was obtained by the mean daily intensity-duration (Imean – D) threshold curve, followed by accumulated rainfall E. This work is the first attempt to estimate regional thresholds on a country scale in order to better understand landslides, and the results obtained reveal the potential of using thresholds in the monitoring and forecasting of shallow landslides caused by intense rainfall and in supporting the actions of disaster risk management

Comparative Ground Validation of IMERG and TMPA at Variable Spatiotemporal Scales in the Tropical Andes

An initial ground validation of the Integrated Multisatellite Retrievals for GPM (IMERG) Day-1 product from March 2014 to August 2015 is presented for the tropical Andes. IMERG was evaluated along with the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) against 302 quality-controlled rain gauges across Ecuador and Peru. Detection, quantitative estimation statistics, and probability distribution functions are calculated at different spatial (0.1°, 0.25°) and temporal (1 h, 3 h, daily) scales. Precipitation products are analyzed for hydrometeorologically distinct subregions. Results show that IMERG has a superior detection and quantitative rainfall intensity estimation ability than TMPA, particularly in the high Andes. Despite slightly weaker agreement of mean rainfall fields, IMERG shows better characterization of gauge observations when separating rainfall detection and rainfall rate estimation. At corresponding space–time scales, IMERG shows better estimation of gauge rainfall probability distributions than TMPA. However, IMERG shows no improvement in both rainfall detection and rainfall rate estimation along the dry Peruvian coastline, where major random and systematic errors persist. Further research is required to identify which rainfall intensities are missed or falsely detected and how errors can be attributed to specific satellite sensor retrievals. The satellite–gauge difference was associated with the point-area difference in spatial support between gauges and satellite precipitation products, particularly in areas with low and irregular gauge network coverage. Future satellite–gauge evaluations need to identify such locations and investigate more closely interpixel point-area differences before attributing uncertainties to satellite products

Projected increases in the annual flood pulse of the Western Amazon

The impact of a changing climate on the Amazon basin is a subject of intensive research because of its rich biodiversity and the significant role of rainforests in carbon cycling. Climate change has also a direct hydrological impact, and increasing efforts have focused on understanding the hydrological dynamics at continental and subregional scales, such as the Western Amazon. New projections from the Coupled Model Inter-comparison Project Phase 5 ensemble indicate consistent climatic warming and increasing seasonality of precipitation in the Peruvian Amazon basin. Here we use a distributed land surface model to quantify the potential impact of this change in the climate on the hydrological regime of the upper Amazon river. Using extreme value analysis, historical and future projections of the annual minimum, mean, and maximum river flows are produced for a range of return periods between 1 and 100 yr. We show that the RCP 4.5 and 8.5 scenarios of climate change project an increased severity of the wet season flood pulse (7.5% and 12% increases respectively for the 100 yr return floods). These findings agree with previously projected increases in high extremes under the Special Report on Emissions Scenarios climate projections, and are important to highlight due to the potential consequences on reproductive processes of in-stream species, swamp forest ecology, and socio-economy in the floodplain, amidst a growing literature that more strongly emphasises future droughts and their impact on the viability of the rainforest system over greater Amazonia

Impacto del cambio climático y uso del suelo en la propagación de la sequía meteorológica a la hidrológica en cuencas amazónicas del Perú

La sequía es uno de los fenómenos naturales más devastadores a nivel global, debido a sus impactos en los sectores económicos y principalmente en la agricultura, afectando a un 40% de la población mundial (FAO, 2022). En esta línea, los cambios de usos de suelo en la propagación de la sequía meteorológica a la hidrológica son de gran importancia para el pronóstico de sequias hidrológicas y prevención y mitigación de desastres por sequía. El presente trabajo tuvo por objetivo: (1) Analizar las características espaciales y temporales de la propagación de la sequía meteorológica a la sequía hidrológica en cuencas amazónicas en el Perú en los últimos 40 años. (2) Determinar los cambios de uso de suelo en la propagación de la sequía meteorológica e hidrológica. La metodología consistió en el cálculo de índices de sequía meteorológica e hidrológica utilizando índices estandarizados a distintas escalas de tiempo, posteriormente se realizó un análisis de correlación de Pearson para medir el grado de relación entre la sequía meteorológica e hidrológica y un método de machine learning para el cálculo de cambios de usos de suelo para la cuenca Intermedio Alto Huallaga. Los resultados mostraron, una tendencia positiva en la sequía meteorológica en los últimos 40 años, este aumento se debe en intensidad de la sequía en todas las cuencas analizadas. En cuanto a la sequía hidrológica ha habido menor intensidad desde los 80s a los 20s pero con mayor frecuencia en las últimas décadas. Así, una fuerte propagación de la sequía meteorológica a la hidrológica en las primeras dos décadas sobre todo en la estación de verano de diciembre a febrero en términos estacionales evidenciando valores de DPI mayores a 1. A la escala mensual según el SPI-1 y el SRI-1 las mayores correlaciones positivas y significativas se tiene con un lag=0, esto indica que si sucede una sequía meteorológica podría suceder en simultáneo un hidrológica en las cuencas andinas amazónicas

Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000-13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network

Trends and space–time patterns of near-surface temperatures on Maxwell Bay, King George Island, Antarctica

There is growing interest in the international scientific community in characterizing climate variability in Antarctica because of the continent's fundamental role in regulating the world's climate. Researchers have intensively studied the Antarctic Peninsula since the warming that began in the mid-1950s. This was followed by a subsequent cooling period over the last decades. For this paper, using the available data, we analyzed the variability in surface air temperatures at five meteorological stations located on King George Island (KGI) (a subantarctic island that is part of the South Shetland Islands); we also investigated the relationships between the air temperatures and large-scale atmospheric patterns from 1968 to 2019. In this study we found that summer temperatures are above 0°C from December to March and close to melting temperatures (extreme values) in spring and autumn; consequently, a small increase in temperature can have a significant impact on the cryosphere. The statistical analysis of the mean temperatures confirmed a trend toward cooling during the summer and in the mean monthly maximum temperatures over the 1990s at most of the weather stations whose data we analyzed. Analyzing the teleconnection patterns showed that the Southern Annular Mode (SAM) had strong, direct, and positive correlations during the autumn and less strong connections in spring, winter, and on an annual scale. Furthermore, we observed a lesser influence of El Niño-Southern Oscillation (ENSO)