Farzin Parchami-Araghi; seyed majid mirlatifi; Shoja Ghorbani Dashtaki; Majid Vazifehdoust; Adnan Sadeghi-Lari
Abstract
Introduction: In order to provide more realistic representation of processes governing the water and energy balances as well as water quality and plant physiological processes, weather data are needed at finer timescales than currently are available at most regions. In this study, a physically based ...
Read More
Introduction: In order to provide more realistic representation of processes governing the water and energy balances as well as water quality and plant physiological processes, weather data are needed at finer timescales than currently are available at most regions. In this study, a physically based framework was developed to disaggregate daily weather data needed for estimation of subdaily reference evapotranspiration, including air temperature, wind speed, dew point, actual vapour pressure, relative humidity, and solar radiation. In this paper, the results of performance comparison of the utilized disaggregation approaches are presented.
Materials and Methods: In developed framework, missing daily weather data are filled by implementation of a search-optimization algorithm. Meanwhile, disaggregation models can be calibrated using Unified Particle Swarm Optimization (UPSO) algorithm. Daily and subdaily solar radiation is estimated, using a general physically based model proposed by Yang et al. (YNG model). Long-term daily and three-hourly weather data obtained from Abadan (59 years) and Ahvaz (50 years) synoptic weather stations were used to evaluate the performance of the developed framework. In order to evaluate the accuracy of the different disaggregation models, the mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (r), and model efficiency coefficient (EF) statistics were calculated. Different contributions to the overall mean square error was decomposed, using a regression-based method.
Results and Discussion: The results indicated that compared to the WAVE I, WAVE II, WCALC, ERBS, and ESRA models, the calibrated TM model had the best performance to disaggregate daily air temperature with a EF of 0.9775 to 0.9924. Compared to air temperature disaggregation models with an arbitrary value for the time of maximum and minimum air temperature, the models in which the above mentioned times are described as a function of sunrise and/or sunset had better performance in describing the diurnal variations of the air temperature. HUM III model (based on cosinusoidal disaggregation of daily actual vapour pressure) had the best performance to disaggregate daily dew point, actual vapour pressure, and relative humidity with an EF of 0.7266 to 0.8896. In addition, subdaily wind speeds were predicted with an EF of 0.3357 to 0.6300. The results showed high agreement between daily and sum-of-subdaily solar radiation (with an EF of 0.9801 to 0.9729). The use of the WAVE II and HUM II (based on linear disaggregation of relative humidity) models can be recommended for the regions with no subdaily weather data needed for calibration of the weather data disaggregation models. The results indicate the need for calibration of Green and Kozek model for disaggregation of the daily wind speed at different regions.
