Journal of Hydrologic Engineering

Predicting water level fluctuations in a lake is crucial in terms of sustainable water supply planning, flood control, management of water resources, shoreline maintenance, sustainability of ecosystem, and economic development. This study developed a new predictive model based on the season algorithm (SA) and multilayer perceptron (MP) methods to improve prediction accuracy and extend water-level lead-time prediction. For the first time, the additive season algorithm (ASA) was used as an alternative data preprocessing technique for predicting water levels, and its performance was compared with that of wavelet transform (WT). The prediction accuracy and longer lead-time predictions were improved by the hybrid additive season algorithm–multilayer perceptron (ASA-MP) model. The results indicated that the hybrid additive season algorithm–multilayer perceptron model can be used to predict monthly water levels accurately with up to a 12-month lead time. The combined wavelet–multilayer perceptron (W-MP) model can be used to predict monthly water levels up to 6 months in advance with good agreement, whereas the standalone multilayer perceptron (MP) model can be used to predict the water levels up to 2 months in advance. The combined additive season algorithm–multilayer perceptron model outperformed the W-MP and standalone MP models based on the mean squared error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as performance evaluation criteria. As opposed to the ASA, the WT method has serious drawbacks and complicated mathematical processes. Furthermore, the prediction performance of the W-MP model was not satisfactory. The results of this study indicated that the ASA-MP model outperformed the W-MP model at all lead times. This implies that the season algorithm can effectively eliminate periodicity and trend cycles from original data better than the wavelet transform. In addition, the MP model should be combined with a preprocessing technique for more-accurate and longer lead-time predictions.

Rainfall-runoff-generating mechanisms in urban green areas are scarcely understood, and limited knowledge and data on rainfall-runoff processes are available. Therefore, a large-scale experimental field station was established to investigate the inherent hydrological processes of a grass-covered $\mathrm{4,300}{\mathrm{m}}^2$ urban catchment consisting of sandy loam soil. A facility to collect surface runoff from the area was designed. Runoff, soil moisture properties, and rainfall were measured simultaneously by a flow meter, in-ground soil sensors, and rain gauges, respectively. Measured soil volumetric water content was above $0.34{\mathrm{m}}^3{\mathrm{H}}_2\mathrm{O}{\mathrm{m}}^{-3}$ soil during fall and winter and ranging between 0.13 and $0.34{\mathrm{m}}^3{\mathrm{H}}_2\mathrm{O}{\mathrm{m}}^{-3}$ soil during late spring and summer. Measured runoff recorded from September 2016 until July 2018 strongly indicates that subsurface throughflow was the dominant runoff type. There was good correlation between the dynamics of soil water content and runoff. Accumulated rainfall and runoff was linearly correlated for soil volumetric water contents above $0.34{\mathrm{m}}^3{\mathrm{H}}_2\mathrm{O}{\mathrm{m}}^{-3}$ soil. The relationship between runoff and rainfall shows a runoff coefficient of 0.18 for the $\mathrm{4,300}{\mathrm{m}}^2$ area.

The challenge of achieving reliable flood forecasting results in semiarid regions remains stark. We developed a flood early warning and forecasting method based on similarity theory and a hydrological model to extend the lead time and achieve dynamic rolling forecasting. A multimeasure-based rainfall event similarity analysis (MRESA) method was proposed for rainfall forecasting based on the similarity evaluation between two rainstorms from multiple perspectives (including the quantity similarity, pattern similarity, earth mover’s distance, and rainstorm spatial distribution similarity). Moreover, an ideal sample experiment was conducted to verify the method’s rationality and feasibility. The MRSA method for rainfall prediction and the vertically mixed runoff model were applied to the Beiniuchuan River located in the middle Yellow River basin. Results showed that the flood forecast would be continuously updated and the prediction accuracy gradually increases with the increase of rainstorm and flood information. Therefore, in this study the proposed flood forecasting method based on the similarity analysis is effective and applicable.

The Manning equation has been widely used in rivers, with the flow resistance caused by bed roughness ($n$) to account for all energy loss influences associated with channel characteristics. Flow resistance also arises from bridge piers, which are common features in rivers, but the Manning equation is inappropriate for estimating overall flow resistance caused by the drag force on bridge piers. Therefore, it is necessary to study the relationship between flow resistance and bridge pier parameters, especially when a large number of bridge piers are present in rivers. Two new methods are proposed, in which the alternative and equivalent roughness $n_t$ is related to bridge pier characteristics and can incorporate bed roughness $n$. The first method (namely measured data–based method) is derived from the analysis of local head loss and frictional head loss and can be used in the case of field-measured data. The second method (namely, the multiparameter empirical method) is derived from the drag coefficient of a single pier, the spatial distribution form of the piers, and the difference of the incoming flow velocities and can be used practically without a comprehensive field site data survey. The calculation of the equivalent roughness of 30 bridges in Jiangsu shows that the results from two methods are in good agreement with each other. The two methods developed in this study provide conveniences for assessing the flow resistance caused by pier drag forces.

This study explored the operational feasibility of an empirical approach to flood inundation forecasting using quantitative precipitation forecasting (QPF) from high-resolution numerical weather prediction models for the city of Houston and the Harris County Flood Control District (HCFCD). A proposed rapid-refresh technique for generating forecasted flood inundation maps was tested, wherein the processing time was limited by the time required for generating high-resolution QPF. Using the dense gauge network operated by the HCFCD, hurricane type storms were found to be generally more challenging for quantitative precipitation forecasting than the less intense and more frequent winter storm events. The investigation also indicated that forecasting inundation is possible based on rainfall forecasts using predeveloped rating curves between the observed rainfall and the expected increase in water level. The median of the relative root mean square error (RMSE) in percentage and the correlation of the forecasted water level at gauge locations are consistently below 10% and higher than 0.7, respectively, for up to a four-day lead time. In terms of spatial detection of flooded (non-flooded) areas, this technique yielded qualitative consistency during peak inundation episodes when the QPF was skillful.

The influence of recent climate variability coupled with the intensified human-induced changes has led hydrologists to modify the flood frequency analysis (FFA) considering nonstationary behavior of time series. Presently, a case study has been undertaken to demonstrate a methodology for FFA considering nonstationarity of annual maximum flood series of six gauging sites from Narmada River Basin. Two different approaches, namely time-varying nonstationarity and nonstationarity due to physical covariates, have been applied in the present case. The influence of climate indices and the effect of reservoir constructions on the Narmada River Basin have been included as external covariates. The results indicated that the construction of reservoirs has played a major role for the changing behavior of flood series of Narmada River Basin from stationary to nonstationary. Due to nonstationarity, the return level flood value increased substantially with time and may create problems for the old structures, which were designed using traditional FFA assumptions of stationarity. The present case study can be used as an alternative to the stationary FFA for any basins worldwide, which will provide more options to the designers and managers.

The Huaibei Plain is one of the most severe water scarcity areas in China. Understanding of hydroclimatic variation in this area at different timescales and its relationship with global teleconnection patterns are important for assessment of water resources utilization. In this study, spatiotemporal changes of seasonal and annual precipitation and temperature, including trend, abrupt change, variability, and periodicity were examined to recognize the potential remarkable changes during the last 41 years. The relationship between precipitation in the Huaibei Plain and teleconnection patterns using climate indexes was revealed by applying singular value decomposition. Results showed a nonsignificant annual precipitation increase about $2.4\mathrm{mm}/\text{year}$. The annual average temperature increased about 1.2°C during 1970–2010. The abrupt change of annual precipitation mainly occurred during the 1970s and 1980s, while the primary mutation points for temperature were detected in 1990s, especially in 1997. The mean areal precipitation is characterized by a statistically significant 2- to 4-year periodicity at different phases, and the 2- to 5-year band is the major cycle for annual average temperature in this region. A statistically strong 5- to 8-year periodicity for precipitation could be detected from the middle of the 1980s to the end of the 1990s. Precipitation has positive correlation with the West Pacific Pattern and El Nino Southern Oscillation. The investigated results might have considerable implications for managing water resources in the Huaibei Plain.

The infiltration capacity of permeable interlocking concrete pavement (PICP) was characterized on five sites located in the greater Montreal area (Canada). Surface infiltration rates up to more than $\mathrm{20,000}\mathrm{mm}/\mathrm{h}$ were observed, even in winter at subzero temperatures (°C). At one of the five monitored sites, rainfall and flow at the outlet were monitored for 12 months. This monitoring revealed peak flow delays ranging from 4 min to 4 h 42 min and runoff reductions ranging from 26% to 98%, depending on the rainfall event. These rainfall and flow data were used to calibrate a PICP hydrologic model that was then used to quantify the impact of implementing PICP in four real urban watersheds. For an eight-year rainfall series, simulations showed a reduction in volume (65%) and duration (21%–48%) of overflows in the two combined sewer systems, a reduction in peak flow (6%–45%) and volume (mean 30%) at the outfall of the two separate systems, and a reduction in surface flooding duration (24%–81%) for the four sewer systems.

El Niño Southern Oscillation (ENSO) is an irregular periodic variation of atmospheric pressure and sea surface temperatures over the tropical eastern Pacific Ocean, affecting climate in most of the tropics and subtropics, which in turn affects water resource availability and crop productivity. Reservoirs have a vital role in management of water resources in regions with multiple cropping patterns. The present study analyzed the impacts of ENSO on reservoirs and its hydrologic components in the Venna river basin of central India. In the present study, the impact of ENSO on water availability in reservoirs was analyzed using spatial analysis of satellite-derived water-spread area (the spatial distribution of water in the reservoir) and its kappa statistics. The normalized difference vegetation index (NDVI) was used to estimate the area of vegetation in catchment and command areas. Analyses reveal that surplus and deficient water available in reservoirs and the area of vegetation in catchment and command areas harmonized with ENSO phases. Analysis of the variation in impacts of ENSO on reservoir-irrigated area (RIA) and groundwater-irrigated area (GWIA) revealed that average NDVI was higher in RIAs compared to GWIAs, and the difference is greater for La Niña and less for El Niño. The onset of greenness is delayed for RIAs as well as GWIAs during El Niño.

Extreme floods are often devastating, directly affecting millions of people worldwide. Because of the lack of infrastructure and proper hydrological monitoring, flood-related hazards in developing countries are magnified. Therefore, regionalization approaches associated with flood indices stand out for allowing reliable design flood estimates and enabling the identification of their driving mechanisms. In this context, the present study aims to evaluate flood timing and regularity using directional statistics for hydrological regionalization. A robust methodological framework was proposed combining fuzzy logic algorithms and the seasonality index, considering maximum annual streamflow data for Southern Brazil. It was found that major floods take place between midwinter and early spring with no well-defined spatial pattern; however, a significant decrease in their regularity was observed westward. Based on these characteristics, two clustering scenarios were proposed. According to the heterogeneity measure, hydrologically homogeneous regions were formed for the entire study area, except for the mountainous region in the northeast. Four main flood-driving mechanisms were identified: frontal systems, maritime air masses, consecutive rainfall days, and antecedent soil moisture.

The magnitudes of peak streamflows and their return periods are important considerations in infrastructure design and hazard mitigation. Periods of high floods are usually associated with seasons and infrastructures and mitigation plans are designed accordingly. Floods, however, are also possible, even though at a much lower frequency, during low-flow seasons. Knowing the actual probability of an extreme event, given the day of the occurrence, can help in the implementation of nonstructural measures for minimizing flood damage, guide the design of more efficient mitigation plans, and optimize resources. Flood risks derived from annual maxima does not give any indication of the actual probability of experiencing a certain flow event in a given season, month, or day. In this note, a simple method of computing the nonexceedance probability of different flows of interest for any day of the year is presented. For example, the nonexceedance probability for a flow of $\mathrm{20,000}{\mathrm{m}}^3/\mathrm{s}$ in the upper reach of the Lower Mississippi River Basin, obtained from water year maxima, is 0.02, but when derived from daily flows, the nonexceedance probability of experiencing such a flow in April oscillates between 0.42 and 0.56. Hence, while a frequency duration curve derived from annual maxima is useful for structural considerations, using the complete daily time series for the computation of the daily probability of flooding provides auxiliary information that can support nonstructural flood mitigation planning.

This note clarifies the confusion in the transformation that transforms the advection-dispersion equation with time-dependent coefficients into the advection-dispersion equation with constant coefficients. It is proved that the analytical solutions based on this transformation cannot satisfy the advection-dispersion equation with time-dependent coefficients.