The application of multiple linear regression method in reference evapotranspiration trend calculation

Khanmohammadi, Neda; Rezaie, Hossein; Montaseri, Majid; Behmanesh, Javad

doi:10.1007/s00477-017-1378-z

Cited by 25 publications

(10 citation statements)

References 30 publications

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“…The elevation dependency of RET changes are due to the elevation-dependent changes of climatic variables. By using the multiple linear regressions, Khanmohammadi et al (2018) demonstrated that RET trend can be calculated by the trends of u 2 , T mean , R s , and VPD ( Figure S2 in Appendix S1). To examine the relative contribution of the change of each meteorological factor, a stepwise regression was completed with the trends of VPD, R s , T mean , and u 2 as predictors, and the trend of RET as the dependent variable.…”

Section: Discussionmentioning

confidence: 99%

How does temporal trend of reference evapotranspiration over the Tibetan Plateau change with elevation?

Zhang

Lei

Chen

2018

Intl Journal of Climatology

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Considerable efforts have been made to determine spatial and temporal patterns and the driving factors for reference evapotranspiration (RET) over the Tibetan Plateau (TP). Yet, how temporal trends of RET vary with respect to elevation is less clear. By using long-term daily meteorological records of 84 stations from 1971 to 2015, this study investigates the elevation dependence of RET trend over the TP. The results show that both increasing and decreasing trends of RET for the study period exist over the TP. The increasing and decreasing trends of RET have significant correlations with elevation, but the absolute changes become smaller in the higher elevation ranges at both the annual and seasonal scales. This is due to the elevation-dependent changes of climate factors, including vapour pressure deficit, mean air temperature, solar radiation, and wind speed, which demonstrate different relationships with elevation. A multivariate linear regression method was used to determine the contributions of the climate factors to RET changes, which indicates that vapour pressure deficit and solar radiation were the primary contributing factors to the increasing trend of RET and wind speed mainly influenced the decreasing trend. This elevation dependence of RET change-rate has important implications for water resources and ecosystems over the TP as well as other highelevation regions, where much of the globe's glacier and snow surfaces are stored. K E Y W O R D Sclimate change, Tibetan Plateau, elevation dependence, reference evapotranspiration

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Section: Discussionmentioning

confidence: 99%

How does temporal trend of reference evapotranspiration over the Tibetan Plateau change with elevation?

Zhang

Lei

Chen

2018

Intl Journal of Climatology

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“…Besides meteorological elements and climatic factors, the geographical distribution of surface water was also affected by the geographic location. By screening various geographic factors related to surface water and applying the multivariate regression analysis, the prediction models of annual and seasonal reference evapotranspiration in the hilly regions in southern China were created [46]. All five models had passed the significance test with confidence coefficient at α = 0.01.…”

Section: Other Driversmentioning

confidence: 99%

Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Liu

Chen

et al. 2019

Water

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This paper has adopted related meteorological data collected by 69 meteorological stations between 1951 and 2013 to analyze changes and drivers of reference evapotranspiration (ET0) in the hilly regions located in southern China. Results show that: (1) ET0 in southern China’s hilly regions reaches its maximum in summer and its minimum in winter, and that the annual ET0 shows an increasing trend. ET0 happened abrupt change due to the impact of abrupt meteorological variables changes, and the significant year of mutation were 1953, 1964 and 2008. Most abrupt changes of ET0 in meteorological stations occurred in the 1950s and 1960s. (2) The low value of ET0 was mainly captured in high-altitude areas. Spatially, the ET0 in the east was higher than that in the west. With the exception of a handful of stations, the trend coefficients of ET0 were all positive, exhibiting a gradual rise. Changes in ET0 in the east were much more sensitive than that in the west. Since ET0 was affected by the cyclical changes in relative humidity, short-period oscillations were observed in all these changes. (3) In general, the ET0 was negatively correlated with relative humidity, and positively correlated with temperature and sunshine percentage. ET0 is most sensitive to changes in average temperature, with a sensitivity coefficient of 1.136. ET0 showed positive sensitivity to average temperature and sunshine hours, which were notable in the northeastern, and uniform in the spatial. ET0 showed negatively sensitivity to relative humidity, and the absolute value of sensitivity coefficient in the northwestern is smaller. The highest contribution to ET0 is the average temperature (6.873%), and the total contribution of the four meteorological variables to the change of ET0 is 7.842%. The contribution of average temperature, relative humidity, and sunshine hours to ET0 is higher in the northern and eastern, northern, northern and eastern areas, respectively. Climate indexes (Western Pacific Index (WP), Southern Oscillation Index (SOI), Tropical Northern Atlantic Index (TNA), and El Niño-Southern Oscillation (ENSO)) were correlated with the ET0. In addition, the ET0 and altitude, as well as the latitude and longitude were also correlated with each other.

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“…3). According to the results of Shiri et al (2013) and Khanmohammadi et al (2018), models in which the RH variable was used showed better results in C and D climates, because the effect of RH on the ET Ref is greater in these climates. Also, based on the results of Yassin et al 2016, the Irmak model (radiation-based) was more accurate in Bw and Bs climates.…”

Section: Refmentioning

confidence: 99%

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

Sharafi

Ghaleni

2021

Theor Appl Climatol

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The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.

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The application of multiple linear regression method in reference evapotranspiration trend calculation

Cited by 25 publications

References 30 publications

How does temporal trend of reference evapotranspiration over the Tibetan Plateau change with elevation?

How does temporal trend of reference evapotranspiration over the Tibetan Plateau change with elevation?

Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

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