Spatial analysis and temporal trends of daily precipitation concentration in the Mantaro River basin: central Andes of Peru

Zubieta, Ricardo; Saavedra, Miguel; Silva, Yamina; Giráldez, Lucy

doi:10.1007/s00477-016-1235-5

Cited by 57 publications

(52 citation statements)

References 32 publications

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“…These precipitation concentration values are similar to the rest of Europe (0.51–0.72) (Cortesi et al ., ), Italy (0.43–0.63) (Coscarelli and Caloiero, ), and other regions in the world such as Algeria (0.47–0.70) (Benhamrouche et al ., ) and New Zealand (0.47–0.70) (Caloiero, ). However, it is higher than Malaysia (0.42–0.58) (Suhaila and Jemain, ) and Peru (0.44–0.56) (Zubieta et al ., ), lower than Iran (0.59–0.73) (Alijani et al ., ) and Chile (0.52–0.74) (Sarricolea and Martín‐Vide, ), and very much lower than southern China (0.74–0.80) (Zhang et al ., ; Fu et al ., ). A recent study of precipitation concentration around the world, by Monjo and Martín‐Vide (), found values from 0.38 to 0.87 for the entire land area, whereas Spain obtained a range of 0.66–0.87.…”

Section: Discussionmentioning

confidence: 99%

Spatio‐temporal variability of daily precipitation concentration in Spain based on a high‐resolution gridded data set

Serrano‐Notivoli

Martín‐Vide

Saz

et al. 2017

Intl Journal of Climatology

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An analysis of the spatial and temporal variability of daily precipitation concentration (CI) in Spain was made based on a high‐resolution (5 × 5 km) daily gridded precipitation data set for the 1950–2012 period. For each grid point in the Iberian Peninsula (IP) and Balearic and Canary Islands, the average annual CI was computed, as well as its coefficient of variation and the 5th and 95th percentiles. Annual values were also computed, and the time series of the index were used to assess temporal trends over the whole period. The spatial distribution of the CI showed a strong relationship with the orographic barriers near the coastlines. The Canary Islands showed the highest values of CI, along with the eastern Mediterranean facade of the IP. The highest inter‐annual variations of the CI occurred in the southern IP and in the southern Canary Islands. The trends of CI were, overall, positive and significant, which indicates an increase of daily precipitation concentration over the study period and an increasing environmental risks scenario where erosivity, torrentiality, and floods may become more frequent.

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Section: Discussionmentioning

confidence: 99%

Spatio‐temporal variability of daily precipitation concentration in Spain based on a high‐resolution gridded data set

Serrano‐Notivoli

Martín‐Vide

Saz

et al. 2017

Intl Journal of Climatology

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“…Data transformations and anisotropy were applied when necessary. This method has been used to interpolate environmental variables, such as rainfall in the Amazon and Andean regions (Guimberteau et al, 2012;Zubieta et al, 2017). To use each precipitation dataset as input to the hydrological model, subdaily data (for example, TMPA datasets with a temporal resolution of 3 h) were rescaled to a daily time step.…”

Section: Datasets Usedmentioning

confidence: 99%

“…It is important to improve these tools, especially because of an intensification of extreme hydrological events in the Amazon Basin (Gloor et al, 2013), such as intense droughts in 2005 and 2010 (Marengo et al, 2008(Marengo et al, , 2011Espinoza et al, 2011) and severe floods in 2009, 2013. Moreover, a high percentage of total annual precipitation can fall in just a few days, causing soil erosion and landslides (Zubieta et al, 2017) In the last two decades, advances in satellite technology have improved rainfall estimation in much of the world (Huffman et al, 2007). The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) precipitation dataset (Huffman et al, 2007) has been important for research and for many hydrological applications in Amazon regions, and there is consensus among studies using TMPA in Amazon regions (Collischonn et al, 2008;Getirana et al, 2011;Paiva et al, 2013;Zulkafli et al, 2014;Zubieta et al, 2015).…”

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confidence: 99%

Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset

Zubieta

Getirana

Espinoza

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In the last two decades, rainfall estimates provided by the Tropical Rainfall Measurement Mission (TRMM) have proven applicable in hydrological studies. The Global Precipitation Measurement (GPM) mission, which provides the new generation of rainfall estimates, is now considered a global successor to TRMM. The usefulness of GPM data in hydrological applications, however, has not yet been evaluated over the Andean and Amazonian regions. This study uses GPM data provided by the Integrated Multi-satellite Retrievals (IMERG) (product/final run) as input to a distributed hydrological model for the Amazon Basin of Peru and Ecuador for a 16-month period (from March 2014 to June 2015) when all datasets are available. TRMM products (TMPA V7 and TMPA RT datasets) and a gridded precipitation dataset processed from observed rainfall are used for comparison. The results indicate that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, compared to observed rainfall (by 11.1 and 15.7 %, respectively). In general, GPM-IMERG, TMPA V7 and TMPA RT correlate with observed rainfall, with a similar number of rain events correctly detected ( ∼ 20 %). Statistical analysis of modeled streamflows indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets in southern regions (Ucayali Basin). GPM-IMERG, TMPA V7 and TMPA RT do not properly simulate streamflows in northern regions (Marañón and Napo basins), probably because of the lack of adequate rainfall estimates in northern Peru and the Ecuadorian Amazon.

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“…This finding is consistent with Zubieta et al . () who found a very similar precipitation concentration (CI 0.44–0.56) using in‐situ meteorological stations.…”

Section: Resultsmentioning

confidence: 99%

“…Here, the contribution of extreme events to rainfall amount is between 27 and 34%, which corroborates recent studies developed by Zubieta et al . () using rainfall data from meteorological stations. Despite the low contribution values over the Andes range, these regions have an important risk of floods, soil erosion or landslides.…”

Section: Discussionmentioning

confidence: 99%

Assessing precipitation concentration in the Amazon basin from different satellite‐based data sets

Zubieta

Saavedra

Espinoza

et al. 2019

Intl Journal of Climatology

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Daily precipitation concentration in the Amazon basin (AB) is characterized using concentration index (CI), which is computed from HYBAM Observed Precipitation (HOP) data set, for 1980–2009 period. The ability of four satellite precipitation data sets (TMPA V7, TMPA RT, CMORPH and PERSIANN) to estimate CI is evaluated for 2001–2009 period. Our findings provide new information about the spatial irregularity of daily rainfall distribution over the AB. In addition, the spatial distribution of CI values is not completely explained by rainfall seasonality, which highlights the influence of different weather systems over the AB. The results of rainfall concentration indicate that the distribution of daily rainfall is more regular over northwest (northern Peru) and central Andes. Conversely, Roraima region and a large area of Bolivian Amazon register the highest irregularity in the daily rainfall. Bolivian Amazon also represents regions where the large percentage of total rainfall arises from extreme events (>90th percentile). Heavy rainfall episodes over Roraima region are induced by humidity influx come from Caribbean region, while heavy rainfall events over Bolivian Amazon and Andes region are induced by the northwards propagation of cold and dry air along both sides of Andes Mountains, but only propagate in all tropospheric levels for the Andes. The results also show that PERSIANN and TMPA7 data sets better estimates the daily precipitation concentration for whole AB, but with a relative error 8%. CI estimated from satellites does not agree well with HOP over the Andes and northern Peruvian Amazon. On the other hand, the temporal variability of CI can partly be detected using CMORPH and TMPAV7 data sets over the Peruvian Andes, and central and southern Brazil. Errors in CI estimating might be related to inaccurate estimation of daily rainfall. Finally, we conclude that satellite‐based precipitation data sets are useful for analysing rainfall concentration in some regions of AB.

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Spatial analysis and temporal trends of daily precipitation concentration in the Mantaro River basin: central Andes of Peru

Cited by 57 publications

References 32 publications

Spatio‐temporal variability of daily precipitation concentration in Spain based on a high‐resolution gridded data set

Spatio‐temporal variability of daily precipitation concentration in Spain based on a high‐resolution gridded data set

Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset

Assessing precipitation concentration in the Amazon basin from different satellite‐based data sets

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