Journal of Hydrologic Engineering

Trash creates numerous problems, including clogging of inlets, aesthetical debasing of neighborhoods, chemical pollution upon degradation, and hazards to aquatic life. Data from 30 stream sections were collected and analyzed for the purpose of developing prediction models for trash counts, volumes, and mass. The analyses resulted in prediction models as a function of adjacent land-cover percentages, channel sinuosity, and the length of wooded buffer zones adjacent to streams. Data were sufficient to develop models for predicting five trash types (plastic bags, plastic bottles, aluminum cans, polystyrene foam, and food packaging). Data were collected for four consecutive seasons, although the effect of season was minimal. The models provided high correlation coefficients and narrow confidence intervals. Variation in counts between two jurisdictions was significant. Since trash studies are needed elsewhere, guidelines for maximizing the quality of the measured data are provided.

The analysis of the climate change impact on flood frequency represents an important issue for water resources management and flood risk mitigation. However, for small/medium catchments ($<\mathrm{1,000}{\mathrm{km}}^2$), the spatial-temporal resolution of global circulation models (GCMs) output is not adequate ($>\mathrm{40,000}{\mathrm{km}}^2$) and downscaling procedures are required. In this paper, two different GCMs selected among the Coupled Model Intercomparison Project 3 models, the Hadley Center Coupled Model, the Parallel Climate Model, and two statistical downscaling approaches, (1) delta change, and (2) quantile mapping, are compared. For the generation of long hourly time series of rainfall, temperature, and discharge, stochastic weather generators coupled with a continuous rainfall-runoff model are employed. Therefore, the frequency of annual maxima rainfall and discharge is projected for the future period 2070–2099 over three small subcatchments in the Upper Tiber River Basin, central Italy. Results reveal that both the GCMs and downscaling methods play a significant role in the determination of the climate change impact for future scenarios, mainly in terms of annual maxima values. By comparing the future (2070–2099) with the baseline period (1961–1990), all GCMs project a decrease of mean annual rainfall ($\sim 30\%$) and an increase of mean annual temperature ($\sim 40\%$). However, in terms of annual maxima (of rainfall and discharge) the results are found to be dependent on the selected GCM and downscaling method. On one hand, through the application of the delta change method, both GCMs project a decrease in the flood frequency curves. On the other hand, if the quantile mapping downscaling method is considered, the Hadley Center Coupled Model 3 projects a decrease in the frequency of annual maxima discharge; the opposite occurs for the Parallel Climate Model. The hydrological characteristics of the study catchments play an important role in the assessment of the climate change impacts. For that, the need to use ensemble GCM results and multiple downscaling methods is underlined.

This paper demonstrates a procedure for deriving the Z–R relationship using poor temporal resolution gauge rainfall data and evaluates its impact on runoff forecasting in the upper Ping River Basin in Northern Thailand. The procedure is based on the use of a scaling logic to modify the Z–R relationship calibrated using daily (or other coarse) resolution ground rainfall data. This scaling procedure is demonstrated using daily gauge data and results in radar rainfall estimates that lead to improved runoff simulations and flood forecasts for the upper Ping River Basin compared with the case in which the daily (or raw) Z–R relationship is used or even when the daily gauge rainfall is used alone. This evaluation is based on hourly comparisons for the high rainfall season over a period of 3 years (2004–2006) at six point locations in the catchment. This scaling relationship has significant implications for flood modeling in most of the developing world that has weather radar coverage and a daily gauge network but a limited continuous ground rainfall measuring network.

The purpose of this paper is to modify the multiscale finite-element method (MSFEM) to solve groundwater flow problems in heterogeneous porous media, such as large-scale problems, long-term prediction problems, and nonlinear problems. The MSFEM has been developed to deal with flows in heterogeneous porous media. Many practical works and numerical simulations have been done to show its accuracy. However, for the large-scale or long-term prediction problems, the MSFEM needs a great amount of computational cost in constructing base functions, which is not efficiency. The primary feature of our modified MSFEM (MMSFEM) is to use a new coarse element subdivision to reduce the number of the interior nodes, thus to decrease the unknowns in the reduced elliptic problems to save much computational cost while ensuring computational accuracy. Some numerical experiments in this paper indicate that the MMSFEM can reduce more than 90% of CPU time of the MSFEM.

The occurrence of macropore flow (MF) has a significant effect on infiltration in the vadose zone. Three heterogeneous column systems were set up in this study to quantify the MF depressional process in unsaturated conditions and its effect on solute transport in various saturated conditions. Series of breakthrough curves were obtained through a miscible displacement experiment with bromide conducted on three parallel undistributed soil columns and a control soil column. Representative mean pore radii (RMPR) were improved to quantitatively separate each soil matric potential region and calculate the effective hydraulic porosity (EHP) for describing the various processes of MF. The resulting two-region model was applied to fit the characteristic parameters of any possible solute transport based on convection–diffusion equations (CDEs). The results show that the transient scenarios of MF could be properly quantified with a modified RMPR (MRMPR) and EHP. With the MF depression, the MRMPR was stable and the number of EHP was sensitive to the MF change before the pore water disconnection at near-saturated conditions. Both the improved RMPR and EHP degraded and the characteristics of two-region flow gradually disappeared because of the exchange between MFs and matric flows. In this course, solute transport was mainly affected by the convective effect of MF. Lateral exchange flows between macropores and matrix pores degraded transport discrepancies when MF transferred to mesoflows at a water head potential of $–10\mathrm{cm}$. The classic CDE parameters for modeling solute transport along the MF depression process are still effective in every test.

Discharge predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. In tidal rivers, water levels are not uniquely a function of streamflow. Here, the possibility to predict discharge from water level information from gauge stations at sea and in the river is explored. A hindcast model is established for a tide-dominated lowland site along the Mahakam River (East Kalimantan, Indonesia), using an artificial neural network (ANN) model. The input data for the ANN are gradually increased, by adding new input data in each step. The results show that the inclusion of data from tide predictions at sea leads to an improved model performance. The optimized ANN-based hindcast model produces a good discharge estimation, as shown by a consistent performance during both the training and validation periods. Using this model, discharge can be predicted from astronomical tidal predictions at sea plus water level measurements from a single station at an upstream location. Alternatively, the ANN model can be used as a tool for data gap filling in a disrupted discharge time-series based on a horizontal acoustic Doppler current profiler (H-ADCP). A forecast model is developed for the same river site that is located near the city of Samarinda. To this end, water level data, predicted tide levels, and at-site historical data are considered as input for the model. The discharge time-series derived from H-ADCP data are used for calibration and validation of the multistep ahead discharge forecast model. A good performance is obtained for predictions with a forecast lead time up to two days.

Objective functions are the most important information source for parameter calibration. Current parameter calibration methods mostly employ error sum of squares as the objective function and depend on the information provided by the objective function to search the parameter optima. This kind of method can increase unrelated local optima for nonlinear models. In order to solve the problem, the linearized calibration method of nonlinear model parameters was developed. The purpose of this paper is to investigate the usefulness of the linearized calibration method in the context of calibrating rainfall-runoff models. The new method was first introduced and then used to calibrate vertically mixed runoff model parameters. Two types of analysis were performed. The first refers to an ideal model case free of data and model errors in which the true optimum set of parameter values was known by assumption. The ideal model case was used to examine whether the linearized calibration method is capable of finding that optimum without producing unrelated local optima. Furthermore, it was also applied to validating the advantages and effectiveness of the proposed method through comparison with the shuffled complex evolution (SCE-UA) method and the simplex method. The second refers to a real case in which the data were from four real catchments. It was used to further examine the performance of the linearized calibration method in model parameter calibration. The results show that the linearized calibration method is always able to find the unique global optima with a fast convergence rate and performs better than the SCE-UA method and the simplex method, which demonstrates that the linearized calibration method is an effective parameter optimization technique.

River ecosystem health assessments are a hot topic in global water resources management. The Huai River Basin is a representative basin faced with serious environmental problems due to high water pollution and numerous water projects. Comprehensive investigation of the factors affecting river ecosystem health is an urgent precursor to the successful management of the Huai River Basin. Based on a series of aquatic ecosystem indicators, monitoring, and standards, such as phytoplankton, zooplankton, and benthic macroinvertebrate data, an assessment framework of river ecosystem health is proposed using a multistep gray correlation evaluation method. Environmental impact factors including climate, land cover, edaphic factors, hydrology, water projects, and water quality were identified using the Kruskal-Wallis test and distance-based redundancy analysis method. The ecosystem was found to be subhealthy with 44% ($31/71$) of sites in a healthy condition, 51% ($36/71$) subhealthy, and 5% ($4/71$) unhealthy. Climate and hydrology were the dominant factors that together contributed 37.5, 24.1, and 6.9% of the variation in phytoplankton, zooplankton, and benthic macroinvertebrates, respectively. The main variables affecting variation were minimum temperature, relative humidity, solar radiation, and the magnitude, frequency, duration, and timing of monthly runoff. Together, these variables explained 24.5% of the variation in the ecosystem health of the whole river. Thus, to improve river ecosystem health in the basin, the regulation of dams and sluices should give priority to ecological outcomes, and the threat of climate change should not be underestimated. This paper provides a blueprint for ongoing ecological monitoring and restoration programs in China, and is expected to be a successful case and reference to transition from water quantity management to river health management in the Huai River Basin.

When conducting a master drainage plan for an urban area, catchments are varied from small to large in size. Although there are many hydrologic methods developed for storm water predictions, the size of catchment often serves as the basis to select a proper method. As always, the common question is how to quantitatively define the size of small watershed, and how to establish the modeling consistency between the rational method for small catchments and the unit hydrograph method for large watersheds. In this study, the volume-based runoff coefficients used in the rational method are theoretically derived, and then calibrated to achieve the best agreement with the unit hydrograph method. The example of the rational method versus the Colorado urban hydrograph procedure was used to demonstrate how to achieve the modeling consistency for the metro Denver area. The same procedure can be extended into the relationships between the rational and kinematic wave methods or the rational and SCS unit hydrograph methods using the local design rainfall depths and soil loss functions. With the established model consistency, the master drainage plan can be implemented using both the rational and unit hydrograph methods for all sizes of watersheds used for drainage designs.

Analysis and forecasting of droughts are important for water resources management. The objective of this study is to analyze hydrological droughts using the joint drought probability distribution derived from the copula theory and analyze drought return periods upstream of Namhan River in the upper Han River basin, Korea. Results of the study shows that the severest drought that occurred from 1981 to 1982 has a 110-year return period.

The alteration of natural flow regimes associated with land use change increases storm water runoff volume, increases peak flows, and reduces the time to peak flow. This can cause flooding, erosion, and higher pollutant loading in streams, rivers, lakes, and estuaries. Rain gardens represent a sustainable and economic method to decrease the volume of water that flows into rivers and streams from impervious areas during storm events. For developments that were built without permanent storm water controls, rain gardens may be a viable retrofit instead of centralized and more costly alternatives. However, there is a lack of knowledge about the performance of rain gardens in urban retrofit applications. In this rain garden study, the hydrologic performance of terraced, street-side rain gardens was examined by monitoring inflow and outflow volumes and water tables during simulated runoff events. Areas of uncertainty that were addressed include general relationships between inflow and hydrologic performance, and analyzing the behavior of the internal saturation zone. The performance variables quantified were runoff volume reduction, reduction in peak flow, and peak delay. For eight simulated runoff events of equivalent rainfall depths ranging from 0.1–1.7 cm, the street-side rain gardens reduced storm water volume by an overall total of 37% with mean individual simulation values for volume reduction, peak flow reduction, and peak delay of 52%, 62%, and 16 min, respectively. Regression relationships between equivalent rainfall depth and volume reduction were developed from the simulated runoff events for rain gardens of different surface area to catchment area ratios. From these relationships, it was conservatively predicted that the rain gardens retained the entire runoff volume for 26% of the 38 natural storm events monitored during the study. The results of this study suggest that rain gardens can benefit existing developments by reducing runoff volume and peak flow, and provide a dynamic internal saturation zone with the potential for water quality benefits. The findings also show the importance of understanding interactions with the in situ soil, the existing drainage system, and the entire catchment area when sizing rain gardens in urban retrofits to meet desired reduction objectives.

Hydrologic extremes such as severe storms, floods, and droughts are inherently seasonal in nature and remain the main concern in designing hydrometric networks. In general, hydrometric networks have been designed without paying particular attention to the effect of seasonal streamflow information (SSI) at gauging stations on the efficiency of the hydrometric networks. This paper evaluates the effect of SSI on streamflow networks based on nonparametric implementation of entropy theory using the kernel density approach for estimating the mutual information between gauging stations on a seasonal basis. Overall, it is shown that the SSI of individual stations is season dependent and the efficiency of the streamflow network is also season dependent, therefore the effect of seasonality should be incorporated in future hydrometric network design. This methodology was applied at five river basins in Canada and its role for network design is discussed.

Groundwater recharge plays a vital role in replenishing aquifers, which impacts on groundwater resources availability. This paper develops a geographic information system (GIS)-based water budget framework in conjunction with a hydrologic model to derive high-resolution spatial-temporal distribution of groundwater recharge for the Southern Hills aquifer system, southwestern Mississippi, and southeastern Louisiana, United States. The framework includes parallel programming to distribute the large amount of software runs to a cluster of supercomputers to expedite computation time. Under the parallel computation, the hydrologic models are calibrated by the USGS WaterWatch runoff dataset and verified by the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration dataset. A map of recharge time lag is derived to understand travel time of infiltrated precipitation reaching the last soil layer. A recharge index map is derived to quantify the percentage of precipitation that becomes groundwater recharge. The results show that the mean annual recharge rate was estimated $47.5\mathrm{mm}/\mathrm{year}$ which was 3.1% of the mean annual precipitation on the area of the Southern Hills aquifer system. High recharge rate was estimated at the outcrops of Miocene deposits in southwestern Mississippi, which are the recharge zones of the deep sands in southeastern Louisiana. This paper successfully demonstrated the applicability of the framework to groundwater recharge estimation on large-scale humid areas.

Studying the relationship of ocean/atmospheric modes with hydrologic cycle in lands would be helpful in hydrologic and meteorological long-lead forecasting, climate fluctuation awareness, and water resources management. One way of measuring these phenomena is to use sea surface temperature (SST) which has received so much attention lately. Local experiments are highly recommended for evaluating effective regions of the seas in correlation with the hydrological responses of the study areas. In this paper, the singular value decomposition (SVD) technique is applied for developing temporal expansion series of Mediterranean SSTs according to the precipitation and streamflow variability of southwest of Iran. These parameters are then used in a nonparametric model, which is modified in a way to deal with the experienced forecasting errors. A lead-time approach was adopted, such that the autumn (October–December) SSTs were correlated with winter (January–March) precipitation and February–May averaged streamflow values. The application of SSTs resulted in better forecast for all stations in dry years rather than wet years. The results showed better capability of SSTs temporal expansion series (first mode) than those of well-known climate indices [Southern Oscillation Index, Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO)] to be used as the predictors of streamflow in the case study of this paper.

Potential evapotranspiration (PET) and reference evapotranspiration (RET) data are usually critical components of hydrologic analysis. Many different equations are available to estimate PET and RET. Most of these equations, such as the Priestley-Taylor and Penman- Monteith methods, rely on detailed meteorological data collected at ground-based weather stations. Few weather stations collect enough data to estimate PET or RET using one of the more complex evapotranspiration equations. Currently, satellite data integrated with ground meteorological data are used with one of these evapotranspiration equations to accurately estimate PET and RET. However, earlier than the last few decades, historical reconstructions of PET and RET needed for many hydrologic analyses are limited by the paucity of satellite data and of some types of ground data. Air temperature stands out as the most generally available meteorological ground data type over the last century. Temperature-based approaches used with readily available historical temperature data offer the potential for long period-of-record PET and RET historical reconstructions. A challenge is the inconsistency between the more accurate, but more data intensive, methods appropriate for more recent periods and the less accurate, but less data intensive, methods appropriate to the more distant past. In this study, multiple methods are harmonized in a seamless reconstruction of historical PET and RET by quantifying and eliminating the biases of the simple Hargreaves-Samani method relative to the more complex and accurate Priestley-Taylor and Penman-Monteith methods. This harmonization process is used to generate long-term, internally consistent, spatiotemporal databases of PET and RET.

The nonlinear Muskingum model is a frequently used hydrologic routing method. In this model, the rate of change of the storage volume with respect to time is expressed by an ordinary first-order differential equation. Generally, this equation has no analytical solution, and thus, should be solved by standard numerical solution methods. Although many optimization techniques have been employed to estimate the parameters for the model, an accurate solution method for calculating the storage time variation of the Muskingum model is still required. Most previous researchers have used an inaccurate explicit Euler’s method along with a manipulated routing equation for calculating the discharge at the downstream end to achieve a better fit for observed data. This manipulation, however, is not acceptable from a mathematical viewpoint. Until now, the storage time variation of the Muskingum model has only been calculated by an explicit Euler’s method; other explicit numerical solution methods have not been used or clearly discussed. All explicit solution methods may produce similar results for small sized time intervals, but in practice for historical field data, the size of the time interval is fixed and insufficiently small; thus, a suitable solution method with sufficient accuracy should be used. This study proposes a fourth-order Runge-Kutta method as an accurate and suitable explicit solution method for calculating the storage time variation of the Muskingum model.