Case Study: Reconstruction of Runoff Series of Hydrological Stations in the Nakdong River, Korea

Abstract

Reliable runoff series is sine qua non for flood or drought analysis as well as for water resources management and planning. Since observed hydrological measurement such as runoff can sometimes show abnormalities, data quality control is necessary. Generally, the data of adjacent hydrological stations are used. However, difficulties are frequently encountered when runoff series of the adjacent stations have different flow characteristics. For instance, when the correlation between the up- and downstream locations in which the stations are located is used as the main criterion for quality control, difficulties can occur. Therefore, this study aims to suggest a method to reconstruct an abnormal daily runoff series in the Nakdong River, Korea. The variational mode decomposition (VMD) technique is applied to the runoff series of the three target stations: Goryeong County (Goryeong bridge) and Hapcheon County (Yulji bridge and Jeogpo bridge). These runoff series are also divided into several intrinsic mode functions (IMFs) that are governed by basin runoff and disturbed flow caused by the hydraulic structure. The decomposition results based on VMD show that the runoff components in a particular station that is influenced by hydraulic structures could be reconstructed using adjacent stations, but the residual mode could not. The runoff reconstruction model using an artificial neural network (ANN), the two “divided” modes, and the residual component is established and applied to the runoff series for the target station (Yulji bridge in Hapcheon County). The reconstructed series from the model show relatively good results, with R2 = 0.92 and RMSE = 99.3 in the validation year (2019). Abnormal runoff series for 2012 to 2013 at the Yulji bridge station in Hapcheon County are also reconstructed. Using the suggested method, a well-matched result with the observations for the period from 2014 onwards is produced and a reconstructed abnormal series is obtained.

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

“…where L signifies the augmented Lagrangian function, ω k denotes the center frequency, (Hz), λ is the Lagrangian multiplier, α represents the penalty factor; and f (t) stands for the original signal. The symbol < > represents the scalar product [26]. The iterative process entails the updating of u k , ω k , and λ.…”

A 500-year runoff reconstruction for European catchments

“…Data-driven methods, Artificial Neural Networks (ANNs; Kwak et al, 2020;Hu et al, 2018;Senthil Kumar et al, 2005) relations. The ANNs consist of artificial neurons organized in layers and connections that route the signal through the network.…”

“…This can be achieved through a process-based model of varying complexity, with the advantage of following general physical laws -e.g., preserving mass balance, etc. Physical based models: MIKE SHE (Im et al, 2009) and VELMA (Laaha et al, 2017) or data-driven methods, such as support vector machines (Ji et al, 2021;Zuo et al, 2020), artificial neural networks (ANNs; Kwak et al, 2020;Hu et al, 2018;Senthil Kumar et al, 2005), random forests (Breiman, 2001;Contreras et al, 2021), and Shannon entropy (Thiesen et al, 2019) The objective of the present study is to provide a long-term, hydrological reconstruction for the Central European catchments, utilizing the available gridded precipitation (Pauling et al, 2006) and temperature (Luterbacher et al, 2004) reconstructions, natural proxies (Ljungqvist et al, 2016) and other long-term historical data sources. Specifically, we use a combination of a conceptual hydrological model (GR1A; Mouelhi et al, 2006) and two data-driven models (Chen et al, 2020;Okut, 2016) to simulate the annual evolution of runoff over the period 1500-2000.…”

Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

“…For example, Chen et al [6], Kabiri et al [7], and Lin et al [8] applied the rainfall-runoff model to assess runoff impacts due to climate and land-use change. Kwak et al [9] also used the rainfall-runoff model to reconstruct the missing runoff time-series information. Similarly, Ballinas-González et al [10] studied the sensitivity analysis of the rainfall-runoff modeling parameters in the data-scarce urban catchment.…”