Yiming Hu

Uncertainty assessment of estimation of hydrological design values

Hydrological frequency analysis is the foundation for hydraulic engineering design and water resources management. However, the existence of uncertainty in sample representation usually causes the uncertainty in quantile or design value estimation. Standard deviation (SD) of the design value with a given non-exceedance probability, as an extremely valuable indicator, is suggested to quantify the uncertainties of hydrological frequency analysis. In order to assess the impact of the sampling uncertainty on design value, in China’s national standard for design flood calculation, an empirical formula for estimating SD of the design value was suggested; however, it is applicable only to the case that Pearson type three probability distribution function (PE3) and method of moment are used to analyze the hydrological samples. In principle, for other types of probability distribution functions and parameter estimation methods, the suggested empirical formula is not suitable. The aim of this article is to propose a general approach to estimate the SD of a design value by using the Bootstrap resampling technique. In order to testify the applicability of the approach, under the condition of PE3 distribution, three kinds of schemes were designed to calculate SD, i.e. the suggested empirical formula in China’s national standard for design flood calculation (SEF), Bootstrap technique with method of moment for parameter estimation and Bootstrap with linear moment for parameter estimation. The annual precipitation observations from 50 gauges around China were analyzed using these three approaches. Results show that the SD values of quantile estimates are significantly different among these three approaches, which means that when parameter estimation methods rather than method of moment are employed, the SD provided by SEF is inapplicable. It also indicates that, in terms of modified design value, i.e., original design value adding security correction value, the proposed method using the Bootstrap method, as a general method for SD estimation, is effective and feasible.

Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River basin

Abstract This paper investigates the impact of surface soil moisture assimilation on the estimation of both parameters and states in the Soil and Water Assessment Tool (SWAT) model using the ensemble Kalman filter (EnKF) method in upper Huai River basin. The investigation is carried out through a series of synthetic experiments and real world tests using a merged soil moisture product (ESA CCI SM) developed by the European Space Agency, and considers both the joint state-parameter updating and only state updating schemes. The synthetic experiments show that with joint state-parameter update, the estimation of model parameter SOL_AWC (the available soil water capacity) and model states (the soil moisture in different depths) can be significantly improved by assimilating the surface soil moisture. Meanwhile, the runoff modeling for the whole catchment is also improved. With only state update, the improvement on runoff modeling shows less significance and robustness. Consistent with the synthetic experiments, the assimilation of the ESA CCI SM with joint state-parameter update shows considerable capability in the estimation of SOL_AWC. Both the joint state-parameter update and the only state update scheme could improve the streamflow modeling although the optimal model and observation error parameters for them are quite different. However, due to the high vegetation coverage of the study basin, and the strong spatial mismatch between the satellite and the model simulated soil moisture, it is still challenging to significantly benefit the runoff estimates by assimilating the ESA CCI SM.

Improving the Distributed Hydrological Model Performance in Upper Huai River Basin: Using Streamflow Observations to Update the Basin States via the Ensemble Kalman Filter

This study investigates the capability of improving the distributed hydrological model performance by assimilating the streamflow observations. Incorrectly estimated model states will lead to discrepancies between the observed and estimated streamflow. Consequently, streamflow observations can be used to update the model states, and the improved model states will eventually benefit the streamflow predictions. This study tests this concept in upper Huai River basin. We assimilate the streamflow observations sequentially into the Soil and Water Assessment Tool (SWAT) using the ensemble Kalman filter (EnKF) to update the model states. Both synthetic experiments and real data application are used to demonstrate the benefit of this data assimilation scheme. The experiment shows that assimilating the streamflow observations at interior sites significantly improves the streamflow predictions for the whole basin. Assimilating the catchment outlet streamflow improves the streamflow predictions near the catchment outlet. In real data case, the estimated streamflow at the catchment outlet is significantly improved by assimilating the in situ streamflow measurements at interior gauges. Assimilating the in situ catchment outlet streamflow also improves the streamflow prediction of one interior location on the main reach. This may demonstrate that updating model states using streamflow observations can constrain the flux estimates in distributed hydrological modeling.

Estimation of design flood using EWT and ENE metrics and uncertainty analysis under non-stationary conditions

Concepts of Expected Waiting Time (EWT) and Expected Number of Exceedances (ENE) have been presented in much literature for estimating the Design Flood (DF) under non-stationary conditions. The parameters of the EWT and ENE are generally no less than four, which inevitably leads to the uncertainty of the DF estimation. In this paper, the Bayesian method is proposed to analyze the impact of parameter estimation uncertainty on the EWT- and ENE-based estimation of the DF and Corresponding Design Reliability (CDR). In addition, a comparison analysis between the EWT and ENE is conducted in terms of the DF and CDR with or without a consideration being given to the impact of parameter uncertainty. In the case of giving no consideration to the impact of parameter uncertainty, the experiment results indicate that the EWT-based estimations are less than that of ENE in terms of DF and CDR in the case of a decreasing trend. While in the case of an increasing trend, the EWT-based estimations are bigger than that of ENE. In the case of considering the impact of parameter uncertainty, results in the case study show that the distribution of the EWT-based estimations of DF and CDR are left shifted compared to that of the ENE. Overall, the EWT-based estimations are significantly different from that of ENE in terms of DF and CDR. Therefore, it is necessary and open for further discussions about which metric will be optimal between the EWT and ENE for estimating the DF under non-stationarity.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Safety assessment for dams of the cascade reservoirs system of Lancang River in extreme situations

Numerous dams have been constructed in the midstream and downstream regions of Lancang River, which form a complex cascade reservoirs system. The safety of dams is critical for water resource management of the whole system. To check the safety of dams, this study used the MIKE 11 model to simulate flood routing along the Lancang River from Xiaowan dam to Jinghong dam under extreme situations of 100-, 500-, 1000-, 5000-, and 10,000-year design floods throughout the whole cascade reservoirs system. The design flood events used as the input for the MIKE 11 model contains the design flood hydrograph of the upstream reservoirs and corresponding flood hydrographs of the intermediate areas. The design flood hydrograph of the upstream reservoirs was obtained using the Equal Frequency Factor Method, and the corresponding flood hydrograph of the intermediate areas was obtained using the Equivalent Frequency Regional Composition Method. The results show that all dams are safe for the 100-, 500-, 1000-, and 5000-year design flood situations throughout the whole cascade reservoirs system, whereas the Manwan and Jinghong dams have a risk of overtopping under a 10,000-year design flood. The curves showing the relationship between the highest water level and return period for the dams are also presented.