Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale

Aguilar, Cristina; Polo, María José

doi:10.5194/hess-15-2495-2011

Cited by 49 publications

(34 citation statements)

References 36 publications

(64 reference statements)

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“…The average annual precipitation and mean, maximum and minimum average daily temperature for the reference period, 1960-2000, were obtained over the study area in Sierra Nevada, together with selected annual snow related-variables that were simulated during this period using the physical and distributed hydrological model, WiMMed (Herrero et al, 2011; http://www.ugr.es/~herrero/wimmed/), developed and calibrated in this region (Herrero et al, 2009(Herrero et al, , 2011Millares et al, 2009;Aguilar et al, 2010;Aguilar and Polo, 2011;Pimentel et al, 2014). The model includes specific spatial interpolation algorithms for the weather variables to include the abrupt topography of this area , and an energy and water balance snow model.…”

Section: Methodsmentioning

confidence: 99%

Extreme values of snow-related variables in Mediterranean regions: trends and long-term forecasting in Sierra Nevada (Spain)

et al. 2015

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Abstract. Mountain areas in Mediterranean regions constitute key monitoring points for climate variability and its impacts, but long time datasets are not always available due to the difficult access to high areas, relevant for capturing temperature and precipitation regimes, and the predominance of cloudy remote sensing images during the snow season. Sierra Nevada National Park (South Spain), with altitudes higher than 3500 m a.s.l., is part of the Global Change in Mountain Regions network. Snow occurrence just 40 km from the seaside determines a wide range of biodiversity, a snowmelt fluvial regime, and the associated ecosystem services. This work presents the local trend analysis of weather variables at this area together with additional snow-related variables. For this, long term point and distributed observations from weather stations and remote sensing sources were studied and used as input and calibration datasets of a physically based snow model to derive long term series of mean and maximum daily fraction of snow covered area, annual number of days with snow, annual number of days with precipitation, mean and maximum mean daily snow water equivalent, and snowmelt and evaporation volumes. The joint analysis of weather and snow variables showed a decrease trend in the persistence and extent of the snow cover area. The precipitation regime, rather than the temperature trend, seems to be the most relevant driver on the snow regime forcing in Mediterranean areas. This poses a constraint for rigorous scenario analysis in these regions, since the precipitation pattern is poorly approximated by climatic models in these regions.

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Section: Methodsmentioning

confidence: 99%

Extreme values of snow-related variables in Mediterranean regions: trends and long-term forecasting in Sierra Nevada (Spain)

et al. 2015

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“…The use of FAO56-PM equation is the recommended method to estimate potential evapotranspiration of a given location. The equation is physically based [1] and therefore provides consistent ET in many regions and climates without any need to estimate additional parameters [6,11]. But it requires many meteorological variables such as input of temperature, wind speed, relative humidity and radiation that limit its widespread use [19].…”

Section: Introductionmentioning

confidence: 99%

Three methods of estimating the power of maximum temperature in TM–ET estimation equation

Mengistu

Amente

2019

SN Appl. Sci.

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The use of FAO56-PM is still the preferred method for the estimation of evapotranspiration (ET) regardless of locations. But in places where data that satisfy all the meteorological variables of PM equation are non-existent or missing, the use of either temperature or radiation-based methods is the alternative option, since both radiation and temperature are the main drivers of ET. From the latter two, temperature-based ET estimation methods are still the simplest ones to use. However, there are challenges since some of the parameters used in the temperature-based equations are location dependent. Such location dependence is due to variability in terms of latitude, altitude and other geo-location parameters. Therefore, local calibration of temperature-based methods is necessary to get better estimates of ET. In this study, one temperature-based ET estimation method known as Temesgen-Melesse's (TM) method was assessed in relation to the PM equation using data of ten Class I meteorological stations in Ethiopia. In the study, three different techniques were tested to find location-dependent maximum temperature power ('n') used in TM equation (i.e., to calibrate the method). The first two techniques involve calibration using PM data. In the first case, monthly averaged data that span from 1 to 5 years were used (as five data sets of 12, 24, 36, 48 and 60 months). In the second case, daily values that span from 10 to 30 days were used in three groups of 10-, 20-, and 30-day data sets. In the third technique, an attempt was made to estimate 'n' from the location latitude, altitude and average of the monthly averaged maximum temperature (T̄m x ). The results obtained from each technique are given as follows. Calibration from the monthly averaged data gave good 'n' values for all the stations with R 2 values ranging from 0.80 to 0.92. There were no differences in the number of data points (12,24, 36, 48 and 60 months) used, which means data points of 12 months are sufficient for the calibration. Calibration using the daily data gave satisfactory 'n' results for all the stations tested, though the statistical parameters and performance tests did not give results that are comparable to the monthly averaged values. The results were nearly the same for 10-, 20-, or 20-day data points, which means any one of them can be used for calibration purposes. Estimation of 'n' from latitude, altitude and T̄m x gave results that are comparable to the ones with 'n' calibrated. The percent differences between the 'n' calibrated using data of 131 months and the 'n' values obtained from monthly data and daily data calibrations are less than 0.16% and 0.5%, respectively. The calculated 'n' showed a percent difference of less than 1.1%. Using calculated 'n' is better than performing calibration using daily data. It can also be considered as option when at least 1-year PM data are not available for calibration.

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“…Como já se tem conhecimento que a equação de HS tende a superestimar a ET0 na região Sudeste do Brasil, são necessários estudos que viabilizam a aplicação da equação de HS, como por exemplo, os estudos realizados em Goiás (FERNANDES et al, 2012), São Paulo (CONCEIÇÃO, 2013 e Pernambuco (LIMA et al, 2013), além de estudos realizados em outros países (AGUILAR; POLO, 2011;GHAMARNIA et al, 2011;SENTELHAS et al, 2010;SPERNA WEILAND et al, 2012;TRAJKOVIC, 2007). Do exposto, o objetivo deste trabalho foi calibrar a equação de Hargreaves-Samani para estimar a evapotranspiração de referência diária no estado do Espírito Santo, utilizandose de diferentes métodos de calibração.…”

Section: Introductionunclassified

Calibração Da Equação De Hargreaves-Samani Para Estimar a Evapotranspiração De Referência No Estado Do Espírito Santo

Zanetti¹,

Dohler

Carmo

et al. 2018

RBAI

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RESUMOA maioria das localidades brasileiras não possui dados meteorológicos disponíveis para estimar a evapotranspiração de referência (ET0) pelo método padrão de Penman-Monteith (PM). Com isso, tornam-se importantes os métodos que possibilitam estimar a ET0 utilizando dados limitados. O presente estudo consistiu em calibrar a equação de Hargreaves-Samani (HS) por diferentes métodos para estimar a ET0 diária no estado do Espírito Santo. Foram utilizados dados meteorológicos de nove estações automáticas do INMET localizadas no estado do Espírito Santo, sendo a ET0 calculada pelo método de PM utilizada como referência para a calibração da equação de HS. A calibração foi realizada utilizando-se os dados de cada estação meteorológica individualmente, por três diferentes métodos: ajuste somente do parâmetro Hc da equação de HS; ajuste dos três parâmetros simultaneamente (Hc, He e Ht); e ajuste da ET0 utilizando regressão linear simples. As equações ajustadas obtidas foram avaliadas através do coeficiente de determinação (r 2 ) da regressão linear simples e a raiz do quadrado médio do erro (RQME). Embora a calibração por ajuste simultâneo dos três parâmetros da equação de HS tenha proporcionado melhores resultados, sua eficiência não diferiu expressivamente dos outros métodos. Dessa forma, considerando as limitações desse método, conclui-se que a calibração usando regressão linear seja o método mais indicado. Como segunda opção, considera-se a calibração usando o ajuste somente do parâmetro Hc da equação de Hargreaves-Samani.Palavras-chave: dados limitados, Penman-Monteith, ajuste empírico de modelos.

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Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale

Cited by 49 publications

References 36 publications

Extreme values of snow-related variables in Mediterranean regions: trends and long-term forecasting in Sierra Nevada (Spain)

Extreme values of snow-related variables in Mediterranean regions: trends and long-term forecasting in Sierra Nevada (Spain)

Three methods of estimating the power of maximum temperature in TM–ET estimation equation

Calibração Da Equação De Hargreaves-Samani Para Estimar a Evapotranspiração De Referência No Estado Do Espírito Santo

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