Modelación hidrológica de la cuenca del río Ilave a partir de datos de precipitación observada y de satélite, periodo 2011-2015, Puno, Perú

Zubieta, Ricardo; Laqui, Wilber; Lavado, Waldo

doi:10.24850/j-tyca-2018-05-04

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…Existen dos trabajos relativamente recientes sobre modelos hidrológicos con imágenes de satélite (Zubieta et al, 2017;Zubieta, Laqui, & Lavado, 2018). Ambos usan imágenes de satélite para la lluvia, pero uno es a escala de tiempo diaria y el otro es mensual.…”

Section: Resultsunclassified

Modelo hidrológico distribuido con imágenes GPM-IMERG en la cuenca del río Huaynamota, Nayarit, México

Espinosa-López

Ibáñez-Castillo

Arteaga-Ramírez

et al. 2020

Tecnol. cienc. agua

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In Mexico, it is necessary to exploit water res ources to cover different needs and to control them to prevent damage caused by extreme events. The objective of this study was to model and calibrate hydrographs calculated with rainfall data measured with GPM-IMERG satellite images in the Huaynamota River Watershed and compare the results with a hydrological model fed rainfall data from automated meteorological stations. The research was conducted in a tributary of the Huaynamota River, which is part of the Lerma-Chapala-Santiago hydrological region. The watershed is located in the states of Zacatecas , Durango, Jalisco and Nayarit. For analysis of the hydrographs at the basin outlet, maximum rainfall events occurring in the periods July 21 to 26, 2016, August 14 to 24, 2017, and September 1 to 16, 2017, were evaluated. The model was developed in HEC-HMS, using methods such 2020, Instituto Mexicano de Tecnología del Agua Open Access bajo la licencia CC BY-NC-SA 4.0

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Section: Resultsunclassified

Modelo hidrológico distribuido con imágenes GPM-IMERG en la cuenca del río Huaynamota, Nayarit, México

Espinosa-López

Ibáñez-Castillo

Arteaga-Ramírez

et al. 2020

Tecnol. cienc. agua

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“…This is still not very frequent in climate change assessments and it was beyond the scope of this study, as the Altiplano climate spatio-temporal variability is sufficiently known [98]. Moreover, limitations on hydrological model performance are likely associated with input data uncertainty, simplified hydrological processes, and the propagation of drought from meteorological to hydrological and agricultural systems [64]. Indeed, the most prominent disagreements are found for the moderate and extreme frequency of agricultural and hydrological droughts characterized by propagation from meteorological droughts [4][5][6].…”

Section: Discussionmentioning

confidence: 99%

“…GR2M is a monthly water balance model [62,63]. The GR2M model has yielded good results in hydrological studies conducted in basins or catchments of the Andes-Amazon region [49], Peruvian Andes [64][65][66], Ecuadorian Andes [67], and Colombian Andes [68]. It has also been applied in Asia [69,70], Europe [71,72], and Africa [73][74][75][76], among other locations.…”

Section: Gr2m Modelmentioning

confidence: 99%

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Comparative Analysis of Climate Change Impacts on Meteorological, Hydrological, and Agricultural Droughts in the Lake Titicaca Basin

Zubieta

Molina

Laqui

et al. 2021

Water

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The impact of climate change on droughts in the Lake Titicaca, Desaguadero River, and Lake Poopo basins (TDPS system) within the Altiplano region was evaluated by comparing projected 2034–2064 and observed 1984–2014 hydroclimate time series. The study used bias-corrected monthly climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), under the Representative Concentration Pathway 8.5 (RCP8.5) emission scenarios. Meteorological, agricultural, and hydrological droughts were analyzed from the standardized precipitation, standardized soil moisture, and standardized runoff indices, respectively, the latter two estimated from a hydrological model. Under scenarios of mean temperature increases up to 3 °C and spatially diverse precipitation changes, our results indicate that meteorological, agricultural, and hydrological droughts will become more intense, frequent, and prolonged in most of the TDPS. A significant increase in the frequency of short-term agricultural and hydrological droughts (duration of 1–2 months) is also projected. The expected decline in annual rainfall and the larger evapotranspiration increase in the southern TDPS combine to yield larger projected rises in the frequency and intensity of agricultural and hydrological droughts in this region.

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“…En lo referente al análisis de precipitaciones, se distribuyeron mediante hietogramas de las EMA aplicados a las EMC; en los últimos años se ha implementado el uso de radares (Méndez-Antonio, Magaña, Caetano, Da-Silveira, & Domínguez, 2009;Méndez-Antonio, Domínguez-Mora, Magaña-Rueda, & Carrizosa-Elizondo, 2006;Magaña-Hernández, Bâ, & Guerra-Cobián, 2013) y satélites (Zubieta, Getirana, Espinoza, Lavado, & Aragón, 2017;Zubieta, Laqui, & Lavado, 2018). Se decidió no utilizar estas dos tecnologías e implementar la señalada por López et al…”

Section: Discussionunclassified

Estimación de áreas vulnerables a inundaciones en zonas urbanas: Morelia, Michoacán, México

Neva

Morales

Vélez

et al. 2020

Tecnol. cienc. agua

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Modelación hidrológica de la cuenca del río Ilave a partir de datos de precipitación observada y de satélite, periodo 2011-2015, Puno, Perú

Cited by 12 publications

References 15 publications

Modelo hidrológico distribuido con imágenes GPM-IMERG en la cuenca del río Huaynamota, Nayarit, México

Modelo hidrológico distribuido con imágenes GPM-IMERG en la cuenca del río Huaynamota, Nayarit, México

Comparative Analysis of Climate Change Impacts on Meteorological, Hydrological, and Agricultural Droughts in the Lake Titicaca Basin

Estimación de áreas vulnerables a inundaciones en zonas urbanas: Morelia, Michoacán, México

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