Journal of Hydrologic Engineering

Rainfall from tropical cyclones (TCs) in Puerto Rico and similar environments can cause extreme riverine flooding accompanied by substantial scour, transport, and sediment deposition. These processes can alter channel and floodplain properties that, in turn, influence the susceptibility to future flooding. The potential of these geomorphic impacts of major floods to influence floodplains (e.g., 100-year flood extent and depth) has received relatively little research attention. Using high-resolution LiDAR topography collected shortly before and shortly after Hurricane Maria in Puerto Rico, this study examines how 10- and 100-year flood extent and flood depth changed in thirteen river reaches in response to Hurricane Maria, a devastating TC that struck Puerto Rico in September 2017. These findings were contextualized by comparing them against floodplain extent and depth changes resulting from peak flow quantile estimates created with and without Hurricane Maria streamflow observations. Changes in the flood extent (depth) associated with geomorphic responses to Hurricane Maria were up to 7% (16%) for 10-year floods. Somewhat smaller changes of up to 4% (8%) were found for 100-year flood extents (depths). On average, these changes were smaller than the peak flow quantile effects for the 100-year flood but greater for 10-year events. These results suggest that, at least in tropical areas affected by TCs, geomorphic changes wrought by major storms should be added to the list of considerations in floodplain mapping and updating. This issue may become more important as climate change continues to intensify TC-induced rainfall and flooding.

This research solves Chow’s linear hydrologic systems equations thoroughly to result in a theoretical instantaneous unit hydrograph (UH), which is a superposition of many ($M$) negative exponential functions. This implies that the instantaneous UH can be imagined as a superposition of many linear reservoirs in parallel. Mathematically, at $M\to \infty$, the theoretical UH (in terms of Taylor series) converges to the writer’s general UH that is a simple analytic expression derived inductively from empiricism. Therefore, this research turns the recent conceptual general UH to a theoretical law that approximates real-world watershed processes as a time-invariant linear hydrologic system. Specifically, we first review Chow’s linear hydrologic systems model and apply it to a conceptual watershed with an instantaneous storm, which results in a theoretical instantaneous UH and an S-hydrograph in the superposition of many negative exponential functions. The resulting S-hydrograph then is shown mathematically to be identical to the writer’s general UH at $M\to \infty$. Finally, the general theoretical UH is applied to 10 real-world watersheds for 19 rainfall-runoff simulations. It is noteworthy that the proposed method has two advantages: (1) it is general for storms with different rainfall durations, and (2) it does not require to define excess rainfall and direct runoff in advance because rainfall losses and baseflow can be a part of the solution process. It is expected that this research provides a deeper understanding of the general UH and thus helps promote its applications in practice.

Methods for the estimation of groundwater level (GWL) are often based on knowledge- and data-based approaches, which are largely affected by the representativeness of input parameters and the quantity and quality of previously collected data sets, respectively. In this study, a new hybrid GWL prediction model combining knowledge- and data-based approaches is proposed using a signal decomposing technique to improve the efficiency and accuracy of GWL prediction with less data dependency. The target site condition was urban areas near a river, where the river stage is the dominant influence on GWL. For this purpose, the engineering groundwater-prediction model (EGPM) as a knowledge-based method and multiple linear regression, artificial neural network (ANN), and wavelet ANN (WANN) as data-based methods were employed and adopted to establish the proposed hybrid GWL prediction model. Case studies in the Korean and Japanese contexts were performed to compare and assess results predicted by existing methods and the proposed hybrid method. It was shown that the proposed hybrid method can enhance the GWL prediction performance with improved accuracy of the prediction and efficiency of database utilization. It was also indicated that the required data length can be alleviated while producing a satisfactory prediction throughout the frequency range of GWL fluctuations.

Undeniably, climate-change-induced sea-level rise has served as a powerful driving force for the occurrence of coastal storms more severe than ever before. Low-lying metropolitan coastal areas, in particular, have become increasingly vulnerable to coastal flooding as a result of the rapid expansion of urbanization along flood-prone areas. Safeguarding coastal communities from the repercussions of flooding through the utilization of best management practices (BMPs) is one of the most effective ways of alleviating the situation. However, solely relying on inshore BMPs might not be the best possible approach when the storm event’s intensity can be reduced before the storm reaches the shoreline through the installment of offshore BMPs. Moreover, many recent studies have confirmed the cost-effectiveness and usefulness of natural elements, especially when combined with engineering solutions. This paper assesses the overall suitability and effectiveness of various combinations of nature-based inshore and offshore BMPs for coastal flood mitigation of storms with different intensities with applications to the Southern Brooklyn area in New York City. Moreover, inevitable conditions stemming from climate variability and change have made the assumption of stationary recurrence of extreme events unreliable. Furthermore, historical events of remarkable impacts have not been considered differently in the attainment of the flood design values. Thus, as one of the main objectives of this study, and in order to consider both, a nonstationary partial frequency analysis is utilized, including the major historical storms jointly with the precipitation data in the study area to obtain 100-year flood design values. To achieve the goals of the study, first, the offshore simulation was carried out using the Delft3D hydrodynamic model, developed by Deltares, in order to find the water level hydrograph for the ungauged areas of interest, with and without offshore BMPs followed by the simulation of the flood events on the inland coastal area using gridded surface subsurface hydrologic analysis (GSSHA), a module of the watershed modeling system (WMS) developed by the USACE, to assess the effectiveness of inshore BMPs. The proposed scenarios are tested against Superstorm Sandy, Hurricane Irene, and the 100-year flood hydrographs. The results showed a remarkable coastal flood wave height and water level reduction when integrating offshore and inshore green BMPs.

A data set comprising rainfall-runoff data gathered at 31 Agricultural Research Service experimental watersheds was used to explore curve number calibration. This exploration focused on the calibrated value and goodness-of-fit as a function of several items: calibration approach, precipitation event threshold, data ordering approach, and initial abstraction value. Calibration methods explored were least-squares, the National Engineering Handbook (NEH) median, and an asymptotic approach. Results were quantified for events exceeding two precipitation thresholds: 0 and 25.4 mm. Natural and frequency-matched data ordering methods were analyzed. Initial abstraction ratios of 0.05 and 0.20 were examined. Findings showed that the least-squares calibration approach applied directly to rainfall-runoff data performed best. Initial abstraction ratios clearly influenced the magnitude of the calibrated curve number. However, neither ratio outperformed the other in terms of goodness-of-fit of predicted runoff to observed runoff. Precipitation threshold experiments produced mixed results, with neither threshold level producing a clearly superior model fit. Frequency-matching was not considered to be a valid analysis approach, but was contrasted with naturally ordered results, indicating a bias toward producing calibrated curve numbers that were 5–10 points larger.

Abstract

Urban stormwater management measures, such as permeable pavement systems (PPSs), have been widely implemented to alleviate increasingly severe urban water-related problems. However, the effectiveness of PPSs in runoff control under different rainfall conditions has not been fully investigated, particularly in China. Therefore, there is an urgent need to study the hydrological benefits of different PPSs in China. In this paper, five PPSs at the Zhenjiang sponge base, including one pervious concrete (PC), two permeable interlocking concrete pavements (PICP1 and PICP2), and two permeable bricks (PB1 and PB2), were selected as study cases. Surface runoff coefficients, underdrain runoff coefficients, total runoff volume reductions, and peak flow reductions during rainfall events throughout the flood seasons of 2019 and 2020 were calculated. The influence of rainfall characteristics, pavement type, and pavement age on PPS hydrological performance were evaluated by statistical analyses ($p<0.05$). The results revealed that the average values of surface runoff coefficients, underdrain runoff coefficients, total runoff volume reductions, and peak flow reductions by the PPSs were approximately 0.025, 0.003, 98.1%, and 96.4%, respectively, in 2019 and changed to 0.045, 0.001, 96.3%, and 92.4%, respectively, in 2020. PC performed best with regard to surface runoff control at the initial stage of operation, followed by PB and PICP. Both rainfall depth and rainfall intensity had a significant correlation with each hydrological performance metric. The increase in surface runoff generation from 2019 to 2020 mainly resulted from the clogging problem, which was closely related to pavement type and surroundings of PPSs. PB performed better than PC and PICP in terms of sustainable hydrological performance. The results provide a scientific reference for the design, construction, and operation of PPSs and can aid with the sustainable construction of sponge cities in China.

Abstract

This study evaluated the runoff reduction performance of five PPSs under natural rainfall events, which were observed during the flood seasons of 2019 and 2020 in Zhenjiang, Yangtze River Delta of China. In total, approximately 96% of the rainfall was captured by the PPSs during the monitoring period, and the PPSs performed better in lower-intensity rainfall events. However, the runoff reduction performance of the PPSs decreased markedly with operation time due to clogging, which is closely related to the pavement type and site surroundings of the PPS. To maintain the function of PPS for runoff control in a long period of time, the permeable brick system is the best type of PPS compared with PC systems and PICP systems. The results indicated that the application of PPSs in sponge city construction could be beneficial in the mitigation of urban floods, and adequate attention should be paid to the clogging problem of PPSs at every stage of PPS design, construction, and operation.

The exacerbation of flood inundation has been highlighted by revolving around climate change, given that this global phenomenon aggravates the flooding status quo and poses a multitude of collateral damages to humans and societies. Climate change leads to changes in precipitation patterns and thereby changes in patterns of discharge and flood inundation. Consequently, estimating these changes using any numerical models is deemed essential to develop countermeasures. There is growing anticipation of climate change over Cambodia and the lower Prek Thnot River Basin, yet it is unknown how serious climate change is on the basin’s hydrological and especially flood characteristics. Thus, this study aims to assess the change impacts by integrating the Rainfall-Runoff-Inundation (RRI) model and the Soil and Water Assessment Tool (SWAT) model to apply in the lower Prek Thnot River Basin, Cambodia, whose susceptibility to climate change is relatively high. The RRI model was integrated with the discharge outputs from the SWAT model to simulate four flood events: 2000, the biggest flood event, 2001, 2010, and 2020 flood events. Hydrographs, water levels, and flood extent maps for these events were generated. The biggest flood event of 2000 was set as the baseline period for a possible flood event in the future projected climate in the 2030s and 2060s using three general circulation models (GCMs) [Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL-CM3), Goddard Institute for Space Studies Model E2, coupled with Russell and interactive terrestrial Carbon Cycle (GISS-E2-R-CC), and Institut Pierre-Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR)] under two representative concentration pathways (RCPs) including RCP2.6 and RCP8.5 emission scenarios. The results of climate change suggested that flood magnitude in the lower Prek Thnot River Basin will vary slightly in the 2030s and greatly in the 2060s. In the 2030s, relative changes in precipitation between $-8.2\%$ and 21.6% could lead to relative changes in extreme flow ($Q_5$, flow exceeding 5% of the time) of $-21.4\%-8.4\%$, changes in peak water level of $-6.3\%-1.3\%$, and changes in inundated area of $-12\%-7.4\%$. In the 2060s, relative changes in precipitation between $-17.7\%$ and 46.4% could lead to relative changes in $Q_5$ of $-39.3\%-13.5\%$, in peak water level of $-19.6\%-5.2\%$, and in inundated area from $-31.4\%$ to 20.7%. These relative changes imply the increases and decreases due to climate change compared with the present climate. Overall, the resultant outputs of this study revealed how impactful climate change is on the basin’s hydrological and flood characteristics, which have long been in question. The results can thus be deployed to underpin decision making and offset the impending negative impacts of climate change on flood inundation within the lower Prek Thnot River Basin.

The Three Gorges Reservoir (TGR) plays an important role in water resource management, and its influence on the downstream flow has attracted much attention. In this work, Dongting Lake was selected as a case study to analyze the influence of the TGR on the hydrological regime. A one-dimensional and two-dimensional (1D and 2D) coupled hydrodynamic model based on Mike simulation modules (MIKE) was constructed for the Yangtze River and Dongting Lake to analyze the hydrological changes induced by the TGR. Hydrological data from 2015 to 2016 were chosen for calibration, and observed data from 2017 were used for validation of the model. The results show that the TGR operation reduced the Jingjiang River inflow and changed the speed of the outflow from Dongting Lake to the Yangtze River during the refill operation period. The methodology for analyzing the influence of the TGR on the hydrological regime using the 1D–2D coupled hydrodynamic model is generic and can thus provide technical support for optimally scheduling the TGR during refill operation.