Predicting streamflows in snowmelt-driven watersheds using the flow duration curve method

Abstract

Abstract. Predicting streamflows in snow-fed watersheds in the Western United States is important for water allocation. Since many of these watersheds are heavily regulated through canal networks and reservoirs, predicting expected natural flows and therefore water availability under limited data is always a challenge. This study investigates the applicability of the flow duration curve (FDC) method for predicting natural flows in gauged and regulated snow-fed watersheds. Point snow observations, air temperature, precipitation, and snow water equivalent were used to simulate the snowmelt process with the SNOW-17 model, and extended to streamflow simulation using the FDC method with a modified current precipitation index. For regulated watersheds, a parametric regional FDC method was applied to reconstruct natural flow. For comparison, a simplified tank model was used considering both lumped and semi-distributed approaches. The proximity regionalization method was used to simulate streamflows in the regulated watersheds with the tank model. The results showed that the FDC method is capable of producing satisfactory natural flow estimates in gauged watersheds when high correlation exists between current precipitation index and streamflow. For regulated watersheds, the regional FDC method produced acceptable river diversion estimates, but it seemed to have more uncertainty due to less robustness of the FDC method. In spite of its simplicity, the FDC method is a practical approach with less computational burden for studies with minimal data availability.

Dynamics of hydrological-model parameters: mechanisms, problems and solutions

“…The abrupt changes in the parameters between two consecutive sub-periods may result in changes in the state variables and fluxes, thereby affecting the simulation results. Hence, all the state variables and fluxes obtained from the different schemes are investigated, and the underlying physical mechanisms are discussed (Kim and Han, 2017).…”

“…Previous studies have demonstrated that the assumption of time-invariant parameters is usually inappropriate. The reasons are that a unique parameter set optimized by hydrological models only represents the average hydrological processes, which do not accurately represent the dynamic response modes of the catchments processes (Pathiraja et al, 2018;Fowler et al, 2018;Zhao et al, 2017;Kim and Han, 2017;Golmohammadi et al, 2017;Delorit et al, 2017;Chen et al, 2017). To investigate the problems caused by time-invariant parameters, a control scheme, i.e., scheme 1, T. Lan et al: Dynamics of hydrological-model parameters: mechanisms, problems and solutions is designed and assessed in this study.…”

“…In view of the compensation between parameters, the most common approach for assessing the dynamics of the hydrological-model parameters is that the calibration period is partitioned into different sub-periods based on the temporal dynamic catchment characteristics (Sarhadi et al, 2016;Merz et al, 2011;Lan et al, 2018;Xiong et al, 2019;Motavita et al, 2019;Deng et al, 2016Deng et al, , 2018Dakhlaoui et al, 2017;Choi and Beven, 2007;Brigode et al, 2013;Kim et al, 2015;Kim and Han, 2017;Zhao et al, 2017;Pfannerstill et al, 2015;Me et al, 2015;Coron et al, 2014;Vormoor et al, 2018;Luo et al, 2012;Guse et al, 2016;Zhang et al, 2011Zhang et al, , 2015Ouyang et al, 2016). The parameter set in each sub-period is optimized to obtain the dynamic parameter team.…”

“…The parameter set in each sub-period is optimized to obtain the dynamic parameter team. Previous studies have demonstrated that sub-period calibration based on the dynamic catchment behavior accurately captures the temporal variations of the catchment characteristics, thereby compensating for structural inadequacy (Lan et al, 2018;Zhao et al, 2017;Kim and Han, 2017;Zhang et al, 2011;de Vos et al, 2010;Gupta et al, 2009;Choi and Beven, 2007;van Griensven et al, 2006;Freer et al, 2003). In the study of Choi and Beven (2007), the subperiods were identified based on different hydrological characteristics using a clustering technique.…”

A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments

“…The input-output consistency can be evaluated using correlation between CPI and observed streamflow as in Westerberg et al (2014) and Kim and Kaluarachchi (2014). The Pearson correlation coefficients between CPI and streamflow data of the 45 catchments had an average of 0.67 with a range of 0.43-0.79, and no outliers were found in the box plot of the correlation coefficients.…”