Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Abstract

This study evaluates the effect of climate change on reference evapotranspiration (ET0), which is one of the most important variables in water resources management and irrigation scheduling. For this purpose, daily weather data of 30 Iranian weather stations from 1981 and 2010 were used. The HadCM3 statistical model was applied to report the output subscale of LARS-WG and to predict the weather information by A1B, A2, and B1 scenarios in three periods: 2011–2045, 2046–2079, and 2080–2113. The ET0 values were estimated by the Ref-ET software. The results indicated that the ET0 will rise from 2011 to 2113 approximately in all stations under three scenarios. The ET0 changes percentages in the A1B scenario during three periods from 2011 to 2113 were found to be 0.98%, 5.18%, and 12.17% compared to base period, respectively, while for the B1 scenario, they were calculated as 0.67%, 4.07%, and 6.61% and for the A2 scenario, they were observed as 0.59%, 5.35%, and 9.38%, respectively. Thus, the highest increase of the ET0 will happen from 2080 to 2113 under the A1B scenario; however, the lowest will occur between 2046 and 2079 under the B1 scenario. Furthermore, the assessment of uncertainty in the ET0 calculated by the different scenarios showed that the ET0 predicted under the A2 scenario was more reliable than the others. The spatial distribution of the ET0 showed that the highest ET0 amount in all scenarios belonged to the southeast and the west of the studied area. The most noticeable point of the results was that the ET0 differs from one scenario to another and from a period to another.

Past and future drought trends, duration, and frequency in the semi‐arid Urmia Lake Basin under a changing climate

“…GCMs are the most reliable tools for modelling climate change (Intergovernmental Panel on Climate Chang [IPCC], 1995), but these models are large‐scale and cannot be used directly (IPCC, 2007). To solve this problem, stochastic weather generators, as a practical and popular method developed and successfully used for different regions of the world, are used (Bayatvarkeshi et al, 2020; Punyawansiri & Kwanyuen, 2020; Thao & Khoi, 2021). The Long Ashton Research Station Weather Generator (LARS‐WG) is one of the most widely applied weather generator models for climate change impact research, which is validated across the world and has been shown to perfectly simulate different climatological conditions (Semenov & Barrow, 1997).…”

Groundwater level fluctuations in coastal aquifer: Using artificial neural networks to predict the impacts of climatical CMIP6 scenarios

“…The larger error for the precipitation compared to the other two parameters may be due to the large variation in precipitation (Hassan et al 2014). Given the results of the model, the maximum temperature was simulated better than the other two parameters and possessed higher accuracy (Bayatvarkeshi et al 2020). Although there is no complete correlation between the models and observations to date, the lack of quite correlation is not a barrier to the climate model's use.…”

Future precipitation changes in the Central Ethiopian Main Rift under CMIP5 GCMs

“…In order to test the performance of the LARS-WG model and ensure its ability to predict future precipitation in this study, the coefficient of determination (R 2 ) and the root-mean-square error (RMSE) were employed to compare observed and simulated rainfall data (Hassan et al 2014;Bayatvarkeshi et al 2020;Koshi et al 2021). R 2 is a dimensionless measure whose optimal value is one, as shown in Equation ( 2).…”