Journal of Hydrologic Engineering

Rainfall–runoff simulation is the basis of basin flood forecasting and water resource planning. However, karst basins are highly nonhomogeneous. With the intermittent uplift of the Earth’s crust, karst landscapes with large depressions and nonhomogeneous vertical orientations have developed in many places, with the surface being covered with depressional soil layers and surrounded by hills. Thus, the springs are recharged in various ways. To better represent the transformation relationship between rainfall, soil water, fissure water, and conduit water, we proposed a conceptual hydrological model called the karst Sacramento (KSAC) model to simulate the rainfall–runoff processes in karst basins. The model couples Sacramento (SAC) and tank models to simulate rainfall–runoff processes in the soil layer of karst depressions and exposed carbonate rock areas, respectively. The KSAC model was applied to the simulation of rainfall–runoff in the Yuquandong (YQD) karst spring basin in central China. The simulation results revealed that the proposed model achieves satisfactory performance in simulating hourly rainfall–runoff processes. The model was further applied to water cycle processes and water resource evaluation studies in the basin. A comparison of models revealed that depressions were more effective in regulating karst groundwater circulation processes. This study can improve our understanding of complex and variable hydrological processes that occur in karst basins.

Bioretention systems are one of the most common low-impact development (LID) facilities. In this paper, we develop a modeling framework that combines the one-dimensional Green and Ampt model with the outlet modeling of weirs, underdrains, bottom, and lateral exfiltration. This framework is specially designed to be applied in poorly gauged watersheds or where continuous simulations are intractable due to a lack of data. First, we calibrate and validate the model using field data. The Nash–Sutcliffe efficiency (NSE) indicator comparing observations with simulations varies from 0.58 to 0.83, while Pearson’s correlation coefficients ($r^2$) vary from 0.91 to 0.98, indicating good agreement. Following, we assess various hydrologic performance indicators using the model by carrying out different analyses such as (1) defining critical rainfall duration for design storms, (2) assessing the flood performance of bioretention designed with predesign methods, and (3) assessing the optimal drainage areas that a bioretention could receive inflows from. Subsequently, we used the model to (4) perform a one-at-time sensitivity analysis identifying the single most sensitive parameters in bioretention hydrologic performance; and (5) assess the combined influence of parameters by performing Monte-Carlo simulations. The results indicate that the optimal impervious area to the drainage area is approximately 6% for the climate of São Carlos - SP. Furthermore, the critical rainfall duration for lot-scale bioretention with a subtropical climate is between 60 and 120 minutes. The bioretention surface area, the depth of the surface layer, and the saturated hydraulic conductivity of the medium were the parameters most sensitive to hydrologic performance. The results of the model comparison with the SWMM software show that both models are similar in infiltration modeling but fundamentally different in percolation conceptualization, causing a difference in the hydrographs between the developed model and the SWMM model. Finally, the results of the design optimization modeling show a simple mathematical procedure to optimally design bioretention by minimizing construction, maintenance, use of land, and other associated costs while guaranteeing expected design hydrological performance.

In open-channel turbulent flows, nonmonotonous distributions (e.g., velocity with dip phenomenon and type-II profiles of suspension concentration distributions) need more careful and appropriate modeling approaches using entropy theory. All existing one-dimensional entropy theory-based models fail to predict such distributions. In this paper, a generalized approach to model the nonmonotonous one-dimensional distributions of certain properties (such as velocity dip phenomenon and type-II suspension distribution) of open-channel turbulent flows is proposed using Renyi entropy theory. This generalized approach is comprised of dividing the whole region of consideration into a finite number of subregions and employing the entropy-based approach to each of these subregions. Using a similar analogy of the principle of maximum entropy (POME), distribution models in one dimension are derived by maximizing the Renyi entropy subject to suitable constraints for all the subregions, and finally, each model of all subregions is combined using an asymptotic matching method to obtain the final single model valid for the whole flow region. This generalized approach is applied to study the vertical velocity distribution with dip phenomenon and type-II profile of sediment concentration distribution for open-channel turbulent flows. These derived models are validated with different sets of experimental data to show the efficiency of the approach. The results show that the proposed model is able to predict the maximum velocity below the free surface, unlike all previous 1D entropy-based velocity models. The results of the error analysis show that on an average, the mean absolute relative error and the root mean square error are reduced by 49% and 54%, respectively. Also, this study derives the very first models of type-II concentration using the entropy theory concept. Error analysis of the proposed type-II model shows that on an average, mean absolute relative error and root mean square error are reduced by 64% and 48%, respectively, compared to the deterministic models. The $R^2$ values of these models were compared, which states that model efficiency can be improved, on average, up to 67% using the proposed methodology. Furthermore, it is found that errors of the velocity prediction from the model with a formula-based dip position are within 2% limit of the errors of velocity prediction with an observed dip position. Apart from these applications, this methodology can also be applied to study other nonmonotonous distributions (such as location of dip position, hindered settling velocity, etc.) that occur in hydrology, environmental engineering, and other fields using entropy theory.

Abstract

The consistency of hydrological sequences has been affected by climate change and human activities, resulting in significant uncertainty in the results of hydrological frequency analysis. The Mann-Kendall test and Hurst coefficient method are used to distinguish and test the trend of hydrological series. Based on the Mann-Kendall rank test and sliding $t$-test, the mutation of the hydrological series is identified and tested. The empirical mode decomposition is used to obtain the trend term, and the consistency correction is performed on the nonconsistent hydrological sequences. The Bayesian model is constructed to estimate the parameters and analyze the uncertainty of the results with the Metropolis-Hastings (M-H) sampling. Taking the annual inflow runoff series of Taolinkou Reservoir, China, as an example, the parameter estimation results of the constructed model are compared with the parameter estimation results of the analytical method. The uncertainty of the parameter estimation results before and after the separation of trend components in the hydrological series is compared. The results indicate that the Bayesian estimation method based on the empirical mode decomposition with the M-H sampling can effectively obtain parameter estimates, and the uncertainty of parameter estimation is relatively small. This study proposes a model that can identify and correct the uncertainty of hydrological series. To reduce the uncertainty of parameter estimation, a Bayesian model is introduced, which can be used for inconsistent hydrological frequency analysis. With the modified original sequence, the interval of parameter estimation becomes smaller, and the uncertainty of parameter estimation decreases. The modified series can reflect the characteristics of the decreasing trend of runoff in the basin. The analysis shows that the design value of Bayesian estimation is more stable and can effectively reduce the design values.

Practical Applications

Due to the impact of climate change and human activities, the consistency of hydrological series has changed, making it difficult for hydrological series to pass data review. To provide support for the design of hydraulic structures, it is necessary to study hydrological frequency analysis methods for nonconsistent sequences. Using the component analysis method, empirical mode decomposition is used to obtain the trend term, and then consistency correction is performed on the hydrological series. M-H sampling Bayesian parameter estimation are used to estimate the parameters and analyze the uncertainty of the results. The research results show that the Bayesian estimation method based on empirical mode decomposition (EMD) and M-H sampling can better obtain the parameter estimation values, and the uncertainty of the parameter estimation of this method is small.

The effects of land-use changes on flooding include both the direct (by changes in the production and confluence parameters) and indirect (by changes in rainfall) effects. In this study, three periods of land-use data were used to analyze the spatiotemporal characteristics of land-use changes in different urbanization periods. The Weather Research and Forecasting (WRF) model was then coupled with the MIKE model to analyze the total effects of land-use changes on watershed flood during different urbanization periods in the Qianshan River Basin, China, including both the direct and indirect effects. In addition, the previous research methods that only consider the direct effects were evaluated by quantifying the proportions of the direct and indirect effects, so as to provide more scientific ideas and references for future related research. The results show that land-use changes increase the peak water level, peak flow, submergence areas, and risk areas in both the pre- and posturbanization period, with the increase in posturbanization exceeding that in preurbanization. This is consistent with the temporal characteristics of urban expansion in the Qianshan River Basin. Moreover, the proportions of the direct and indirect effects of land-use changes on the peak water level and peak discharge are similar, while the direct effects on the submergence and risk areas dominate those of the indirect effects. When only considering the direct effects, the increases in peak water level, peak discharge, submergence areas, and risk areas are reduced by 45%–55%, 57%–62%, 23%, and 19%, respectively. Therefore, both the direct effects and indirect effects of land-use changes must be considered in future related research.