Journal of Hydrologic Engineering

Water quality in the four major river basins in South Korea has been managed by introducing total maximum daily loads (TMDLs) to achieve or maintain a target water quality (TWC); TMDLs can be obtained by discharging the allocated pollutant loads from the watershed. The Nakdong River basin is the most important source of water supply among these four river basins and consists of 41 watershed units. The current method for evaluating the rate of load management involves calculating the measured values over the past 3 years through a log transformation process and assessing whether the TWC is achieved after comparing the values with the reference TWC. The objective of this study is to compare and analyze the results of a flow duration curve (FDC) and load duration curve (LDC) evaluation based on the current method for TWC evaluation and the daily flow data measured at 8-day intervals (weekly) to present an efficient TWC assessment method for more relevant flow conditions. The daily flow data were from 1995 to 2004, and the flow data measured at 8-day interval were from 2005 to 2015. The target study areas included the 41 watershed units on the Nakdong River basin (14 main streams and 27 tributaries). Biochemical oxygen demand (BOD) and total phosphorus (TP) constituted the water quality indicators studied. LDCs were prepared using the estimated daily flow rate, and the measured flow data at 8-day intervals based on the BOD and TP revealed similar results. Thus, even the data measured at 8-day intervals are considered applicable for creating FDCs and evaluating LDCs without requiring complex tests, calibrations, and prediction processes using a watershed model based on accumulated data over many years.

Recent studies have provided explicit analytical equations that can be used to quantify directly the hydrologic performance statistics of green roofs, such as runoff-reduction ratios. These equations were obtained based on simplified representations of the hydrologic and hydraulic processes occurring on and inside green roofs, as well as stochastic models describing local rainfall characteristics. To simplify derivations, these studies considered only saturation-excess runoff and neglected infiltration-excess runoff that may be generated from green roofs. We develop a method for considering both saturation- and infiltration-excess runoff; the proposed analytical equations can be used to directly quantify the performance statistics of any type of green roof. Systematic comparisons of analytical and numerical simulation results were also conducted to demonstrate the accuracy of the analytical equations. As an alternative to numerical simulations, the analytical equations can be used by engineers to more conveniently quantify the performances of alternative green roof configurations.

Streamflows derived from hydrological models are widely used in decision-making processes in a broad array of natural resources applications. There remain substantial challenges in quantifying error and uncertainty in hydrological models, but understanding the sources and magnitudes of error and uncertainty are essential to support robust decision making. In this study, the accuracy of a mixed-effects model for streamflow (flow-duration curves) across the state of Wisconsin, the Natural Community Model (NCM), was evaluated. The NCM is used as the basis for scientific studies and management decisions in Wisconsin, but uncertainty in the NCM has not yet been quantified, and performance has not been assessed formally except at continuously monitored streamflow stations. Although there are a couple hundred long-term monitoring stations, there are thousands of short-term and sporadic monitoring stations in Wisconsin. To take advantage of the vast number of sparsely monitored and short-term stations, an index gauge approach was used to estimate long-term streamflow percentiles and flow-duration curves (with uncertainty). These flow-duration targets formed the basis for an assessment of NCM accuracy in ungauged streams. A random forest model for NCM error was developed that provides a qualitative understanding of sources of error in the NCM as well as a quantitative way to correct the NCM using information from the sporadic/short-term streamflow stations that could not be included in the original NCM training set. By combining the original NCM and the random forest model, an updated NCM was produced with reduced error (75th percentile of errors dropped from 0.23 to $0.07{\mathrm{m}}^3/\mathrm{s}$), and uncertainty estimates were defined for use with the updated NCM in decision making and research applications.

Error correction is an important postprocess technique in hydrological modeling. The paper aims to investigate the effectiveness and suitability of a recently proposed error correction method, the regularized dynamic system response curve (RDSRC) method in the Hydrologiska Byråns Vattenbalansavdelning (HBV) model. In the HBV model, the effective precipitation can reflect the error of model inputs so it is used for runoff error correction. Three commonly used statistics [Nash-Sutcliffe efficiency (NSE) coefficient, root-mean-squared error (RMSE), and percent bias (PBIAS)] were used to evaluate the model performance. In this study, the RDSRC method was tested by a synthetic case and a real case. Results of the synthetic case showed that the NSE, RMSE, and PBIAS of runoff simulation before correction were 0.943, $182.73{\mathrm{m}}^3/\mathrm{s}$, and $-0.626\%$, respectively, while those statistics after correction by the RDSRC method were $\mathrm{1,0.61}{\mathrm{m}}^3/\mathrm{s}$, and 0.002%, respectively. In the real case, the HBV model was used for daily runoff modeling in the Shiquan catchment in China; meanwhile, the RDSRC method and the AR(2) model were compared for runoff error correction. The corrected daily runoff by the RDSRC method had much better accuracy than the second-order autoregressive [AR(2)] model and the HBV model. After correction of the RDSRC method, the NSE of the HBV model increased from 0.83 to 0.946, whereas the RMSE of the HBV model decreased by 43.6%, from 246.37 to $138.96{\mathrm{m}}^3/\mathrm{s}$. This case study provides two main conclusions: (1) the RDSRC method had better performance in runoff error correction than the AR(2) model, and (2) the RDSRC method is an effective alternative for daily runoff error correction when used for the HBV model.