Simulation of Agricultural Water Supply Considering Yearly Variation of Irrigation Efficiency

Abstract

The objective of this study was to evaluate simulation of agricultural water supply considering yearly variation of irrigation efficiency. The water supply data of the Idong reservoir from 2001 through 2009 was collected and used for this study. Total 6 parameters including irrigation efficiency (), drainage outlet height, and infiltration, were used for sensitivity analysis, calibration, and validation. Among the parameters, the  appeared to be the most sensitivity parameter. The  was calibrated on a yearly basis considering sensitivity and time-varying characteristic, while other parameters were set to fixed values. The statistics of percent bias (), Nash-Sutcliffe efficiency (), and root means square error to the standard deviation of measured data () for a monthly step were 2.7%, 0.93, and 0.26 for the calibration, and 3.9%, 0.89, and 0.32 for the validation, correspondently. The results showed a good agreement with the observations. This implies that the modeling only with appropriate parameter values, apart from modeling approaches, can simulate the real supply operation reasonably well. However, the simulations with uncalibrated parameters from previous studies produced poor results. Thus, it is important to use calibrated values, and especially, we suggest the 's yearly calibration for simulating agricultural water supply.

Lessons from Assessing Uncertainty in Agricultural Water Supply Estimation for Sustainable Rice Production

“…From 2001 to 2013, the Korea Rural Community Corporation (KRC) measured the water level every 10 min, using ultrasonic sensors installed at the head of the main irrigation canal (Figure 1b). The observations were subsequently converted to discharges using the stage-discharge relationship developed for the point (Figure 1c) [20]. The amount of water supplied to the irrigation district was compared with predicted AWS to assess the performance and uncertainty of the AWS model.…”

“…subsequently converted to discharges using the stage-discharge relationship developed for the point (Figure 1c) [20]. The amount of water supplied to the irrigation district was compared with predicted AWS to assess the performance and uncertainty of the AWS model.…”

“…The IWR for rice paddy fields can be determined by estimating the daily water demand necessary to maintain the optimal water levels for paddy rice growth during each cropping period; this is calculated as a function of meteorological data and farming practice parameters [8,9,12,19]. The DMWR is usually calculated by adopting the irrigation efficiency (Es) parameter [8,10,20]. However, the actual AWS is controlled by the managers (or operators) of irrigation facilities, considering water demand estimates based on their customary experience, and is not usually determined by calculating the IWR.…”

“…These parameters are associated with the characteristics of irrigation districts and farm management practices, which are highly variable depending on the hydrological characteristics [22]. Several studies have applied AWS models to estimate water requirements; however, these sensitive parameters were determined on the basis of the literature [7,9,23,24], which has often led to substantial errors in the estimates [9,20].…”

“…Im et al [25] and Song et al [8] linked an AWS model to a reservoir routing model, and then calibrated the AWS parameters by setting the reservoir water levels as a calibration target to estimate the AWS. However, in previous studies, the parameter sensitivity and uncertainty in agricultural system modeling have not been sufficiently discussed to provide a clear idea of their impacts on AWS determination and the following decision-making processes, even though equifinality issues have been discussed in the field of hydrological modeling [8,20,[26][27][28]. In particular, the irrigation efficiency in rice paddy fields has been reported to vary substantially every year, and small changes in the irrigation efficiency could lead to large differences in the AWS estimate and the following management and construction decisions [8].…”