Precipitation extremes have been investigated throughout the last decades in different regions of the Andes. However, little attention has been paid to the Altiplano region (Central Andes), especially to the Peruvian Altiplano (PA) that represents a complex area and is highly vulnerable to extreme events, such as floods and droughts, driven by the strong variability of precipitation. This study focuses on the analysis of 11 extreme precipitation indices (EPIs) in the period 1971–2013. In this context, commonly used statistical trend and break analyses were applied and a false discovery procedure was used in order to reduce the number of artificial significant tests. Additionally, the relative dominance of precipitation frequency and intensity in interannual precipitation datasets was determined. Finally, the correlation between EPIs and six oceanic‐atmospheric indices were analysed. ...

Publicación realizada en el marco del convenio específico entre SENAMHI y SUNASS.

Publicación realizada en el marco del Proyecto de Apoyo a la Gestión del Cambio Climático (Fase 2). Una iniciativa del gobierno peruano, liderado por el Ministerio del Ambiente y el Servicio Nacional de Meteorología e Hidrología del Perú.

This study provides for the-first-time a water availability analysis at drainage and basin-scale in Peru. Using new gridded datasets of precipitation and temperature, along with six global actual evapotranspiration estimations from remote sensing products, the vulnerability of water resources due to climate change is evaluated. This is addressed under a bottom-up approach and probabilistic Budyko framework that enables us to measure the associated uncertainty. First, to select an adequate estimation of long-term actual evapotranspiration, we compared at basin-scale the remote sensing products with long-term actual evapotranspiration inferred from a waterbalance (precipitation minus discharge) and deterministic Budyko (aridity and evaporative index relationship). Later, the probabilistic Budyko is calibrated using the adequated remote-sensed actual evapotranspiration and is cross-validate...

Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981–2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adeq...

A new FAO Penman-Monteith reference evapotranspiration gridded dataset is introduced, called PISCOeo_pm. PISCOeo_pm has been developed for the 1981–2016 period at ~1 km (0.01°) spatial resolution for the entire continental Peruvian territory. The framework for the development of PISCOeo_pm is based on previously generated gridded data of meteorological subvariables such as air temperature (maximum and minimum), sunshine duration, dew point temperature, and wind speed. Different steps, i.e., (i) quality control, (ii) gap-filling, (iii) homogenization, and (iv) spatial interpolation, were applied to the subvariables. Based on the results of an independent validation, on average, PISCOeo_pm exhibits better precision than three existing gridded products (CRU_TS, TerraClimate, and ERA5-Land) because it presents a predictive capacity above the average observed using daily and monthly data...

Seminario: Estudios e Investigaciones Hidrológicas SENAMHI, 23 de Enero del 2015.

In soil erosion estimation models, the variable with the greatest impact is rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000-2020. By using this method, a correlation of 0.7 was found between the PISCO_reed and RE o...

In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (), which relates to precipitation. The requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for estimation. This study evaluates the performance of a new gridded dataset of and in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and obtained by the observ...

A new gridded rainfall dataset available for Peru is introduced, called PISCOp V2.1 (Peruvian Interpolated data of SENAMHI’s Climatological and Hydrological Observations). PISCOp has been developed for the period 1981 to the present, with an average latency of eight weeks at 0.1° spatial resolution. The merging algorithm is based on geostatistical and deterministic interpolation methods including three different rainfall sources: (i) the national quality-controlled and infilled raingauge dataset, (ii) radar-gauge merged precipitation climatologies and (iii) the Climate Hazards Group Infrared Precipitation (CHIRP) estimates. The validation results suggest that precipitation estimates are acceptable showing the highest performance for the Pacific coast and the western flank of the Andes. Furthermore, a meticulous quality-control and gap-infilling procedure allowed us to reduce the forma...

The preliminary results showed that random forest worked best for the PB imbalanced data, having a 0.84 weighted average in precision and recall metric. The model reproduces 9 of the PB with low error 4.5% and overestimates 34.52 % one of them in the Amazon. Furthermore, there is an increasing slight trend (not significant) of AI at the drainage-scale, mainly in the Pacific. We hypothesize that there is a migration of dryland subtypes from dry to wet areas in the present time.

Presenta información sobre datos de precipitación para la toma de decisiones relacionadas con los riesgos del cambio climático y la reducción de estos a través de la mitigación y adaptación. También incluye los datos interpolados de Perú provenientes de las estaciones climatológicas e hidrológicas del SENAMHI (PISCO).

Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature and precipitation. About 40ĝ% of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of statio...

Precipitation deficits remain a concern to the rural population in the southern Peruvian highlands and knowledge about their occurrence is lacking because of scarce data availability. For mountainous regions with sparse station networks, reanalyses can provide valuable information; however, known limitations in reproducing precipitation are aggravated due to unresolved topographical effects. In this study, we assess in a first step the representation of precipitation during the rainy season (January–February–March) in seven reanalysis data sets in comparison to a newly generated gridded precipitation data set for Peru. In a second step, we assess summer precipitation deficits in Peru during the second half of the 20th century. In the reanalyses data sets, we find biases strongly influenced by the topography of the models and low correlations for the rainy season. Thus, reanalyses do ...

Los estudios, investigaciones y sistemas operacionales de monitoreo y pronóstico hidrometeorológico tienen el propósito de generar información sobre las características climáticas e hidrológicas que contribuyan a la comprensión de la hidroclimatología de las cuencas y la vigilancia de eventos extremos. La producción de estos productos demandan gran inversión de tiempo en el tratamiento y crítica de los datos para asegurar buena calidad, continuidad temporal de las series pluviométricas, homogeneidad, factores que son una limitante para atender con oportunidad diferentes demandas de información para diferentes usuarios; por otro lado la baja densidad de las estaciones meteorológicas en el país amerita la utilización de procedimientos de regionalización e interpolación espacial para generar información en sitios no instrumentados, todo ello conlleva al uso de diferentes...

In the southern Peruvian Andes, communities are highly dependent on climatic conditions due to the mainly rain-fed agriculture and the importance of glaciers and snow melt as a freshwater resource. Longer-term trends and year-to-year variability of precipitation or temperature severely affect living conditions. This study evaluates seasonal precipitation and temperature climatologies and trends in the period 1965/66–2017/18 for the southern Peruvian Andes using quality-controlled and homogenized station data and new observational gridded data. In this region, precipitation exhibits a strong annual cycle with very dry winter months and most of the precipitation falling from spring to autumn. Spatially, a northeast–southwest gradient in austral spring is observed, related to an earlier start of the rainy season in the northeastern part of the study area. Seasonal variations of maximum ...

Hydrological hazards related to flash floods (FF) in Peru have caused many economic and human life losses in recent years. In this context, developing complete early warning systems against FF is necessary to cope impacts. For this purpose, hydrological and hydraulic models coupled to numerical weather models (NWM) that provide forecasts are generally used. In this sense, the National Meteorological and Hydrological Service of Peru (SENAMHI) has launched the ANDES initiative (Operational Forecasting System for Flash Floods of SENAMHI in English) to support FF events.

In the southern Peruvian Andes, climatic threats such as water scarcity or frost pose major challenges for agriculture. Such events may result in severe yield losses threatening the livelihood of smallholder farmers due to missing adaptive and coping strategies. Knowledge on climate variability and change, on the current state of the climate, as well as short- to midrange predictions potentially improve the farmers’ risk management. However, such knowledge is only partly available and often does not reach rural communities. Climandes, a pilot project of the Global Framework for Climate Services, tackled these shortcomings through the enhancement of climatological observations, the production of gridded datasets using satellite and station observations, the verification of seasonal forecasts to determine their usefulness for small-scale applications, and through the establishment of com...

Background: Global temperatures are projected to rise by ≥2 °C by the end of the century, with expected impacts on infectious disease incidence. Establishing the historic relationship between temperature and childhood diarrhea is important to inform future vulnerability under projected climate change scenarios. Methods: We compiled a national dataset from Peruvian government data sources, including weekly diarrhea surveillance records, annual administered doses of rotavirus vaccination, annual piped water access estimates, and daily temperature estimates. We used generalized estimating equations to quantify the association between ambient temperature and childhood (< 5 years) weekly reported clinic visits for diarrhea from 2005 to 2015 in 194 of 195 Peruvian provinces. We estimated the combined effect of the mean daily high temperature lagged 1, 2, and 3 weeks, in the eras before (200...