Uncertainty in Irrigation Return Flow Estimation: Comparing Conceptual and Physically-Based Parameterization Approaches

Abstract

Irrigation return flow (RF) is a critical component of the water cycle in an agricultural watershed, influencing the flow regime of downstream river. As such, it should be accurately quantified when developing water resources management plans and practices. Although many studies have proposed ways to quantify RF, uncertainty in RF estimates has not been determined to improve reliability and credibility. This study examines how conceptual (CON) and physically-based (PHY) parameterization approaches affect RF uncertainty. Results showed that PHY had a smaller amount of RF uncertainty compared to CON, as parameters of the PHY approach could be regulated based on their physical meanings. This study also found that the application of constraints created based on the relationship between the conceptual parameter and physical characteristics of irrigated plots could effectively reduce RF uncertainty made using the CON approach. This study demonstrates the benefits of the physically-based parameterization approach and the application of constraints on conceptual parameters to RF estimation.Water 2020, 12, 1125 2 of 14 rainy days. A modeling approach has been recommended to effectively differentiate runoff by rainfall and RF by irrigation in runoff drained from a plot [3,16,17].RF can be estimated by simulating the overall water balance, which consists of rainfall, irrigation, surface drainage, evapotranspiration, and infiltration. Surface drainage is a key variable for quantifying RF, and two parameterization schemes are commonly used to describe surface water drainage processes: conceptual and physically-based approaches. The conceptual approach (CON) employs parameters that are conceptually defined (rather than physically) using the linear reservoir theory [11,[18][19][20][21]. On the other hand, a physically-based parameterization approach (PHY) represents the drainage processes using the broad-crested weir equation [3,7,[22][23][24][25].Parameterization schemes have their own strengths and weaknesses in terms of complexity, accuracy, and uncertainty. Even when there are no drainage observations for calibration, for instance, the parameter values of PHY may be accurately estimated from their physical meanings. In contrast, it can be difficult to reasonably determine the values of conceptual parameters without calibration due to the scale-dependency of system heterogeneity and nonlinearity [26]. In the case of an ungauged watershed, the applicability of conceptual parameters has not received sufficient systematic analysis to guide the selection of approaches.This study evaluated the applicability of conceptual and physically-based parameterization approaches for estimating RF of ungauged watersheds. The accuracy and uncertainty of RF estimates made using the two approaches were compared to each other. We also discuss how expert knowledge can help refine the value range of a conceptual parameter, aiding the reduction of uncertainty in the conceptual approach [27,28]. This study demonstrates how hydrological reasoning ca...