Journal of Hydrologic Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Climate change (CC) is considered one of the most critical threats to human lives and activities due to dramatic increases in the frequency and severity of droughts and floods under global warming. To alleviate such impacts, many studies on CC adaptation strategies in water management have emerged. This review covers 131 relevant studies published over the past two decades. It aims to robustly synthesize the applied strategies/techniques and identify findings and gaps. In addition, a bibliometric analysis is performed to describe the co-citation network and statistical characteristics of the reviewed papers and identify the related research clusters. A typical procedure for CC adaptation studies is proposed based on previous studies. It is found that systems reoperation was preferred for CC adaptation in water resources management, specifically by updating the reservoir operation curves using optimization algorithms. However, low impact development (LID) measures were favored in storm drainage and flood mitigation systems. As for future relevant research, the main recommendations are integrating environmental, social, and economic aspects in evaluating CC adaptation strategies and incorporating land use and land cover (LULC) and water demand changes in CC adaptation studies. This state-of-the-art review represents essential information for improving CC adaptation strategies in water management.

The zero-inertia approximation of the shallow water equations has been observed to provide reasonably accurate results for various flood propagation problems. The commonly adopted explicit time schemes in the numerical modeling of zero-inertia flows are computationally expensive and vulnerable to numerical instabilities. This paper presents a finite volume method-based zero-inertia overland flow model named surfaceFlowFOAM, developed using OpenFOAM framework with an implicit time discretization scheme. The nonlinear zero-inertia equation is linearized by applying the iterative Picard linearization technique. We have implemented a stabilized and adaptive time-stepping algorithm for controlling the convergence of Picard iterations to avoid numerical instability, thereby enhancing the overall computational efficiency. This study analyzes that the applicability of the zero-inertia approximation should be limited to low subcritical surface flow problems with Froude numbers less than 0.5. The analysis has been done by observing the effects of variations in bed-slope and rainfall intensity on the Froude number of flows developing over the overland surface. Furthermore, we have discussed the achieved mass balance accuracy of surfaceFlowFOAM in solving the considered test examples. The numerical results of surfaceFlowFOAM show excellent correspondence with the results presented in the literature for solved overland flow problems. The solution accuracy and the achieved computational efficiency ensure the potential applicability of surfaceFlowFOAM to solve the lower range (Froude number $<0.5$) of two-dimensional subcritical flood flow problems.

Abstract

Global climate change and land use change are the two main factors influencing the hydrological cycle. Under future climate change conditions, the consequential change of hydrological extremes and design flood values of basins will directly influence the operation and dispatch of cascade reservoirs. Therefore, studying the impact of climate change on design flood values is crucial for flood defense and hydropower output increment. Annual maximum (AM) sampling was employed to obtain the annual maximum daily flood series of the Xiaodeshi station in the future (up to the year 2100) based on soil and water assessment tool (SWAT) modeling of daily runoff. Then, a time-varying moment model with time and meteorological factors taken as the covariates was developed, and the parameters were optimized based on the particle swarm optimization (PSO) algorithm. Subsequently, the design flood values with different frequencies were calculated, varying annually from 2022 to 2100. To align with the present reservoir operation protocol, a new method was developed based on the equivalent reliability (ER) principle to calculate the unique design flood values based on the dynamic outputs. The results indicate that, compared with using time as the covariate, the time-varying moment model with meteorological factors as covariates is physically more significant and yields more reasonable results. Under future climate change conditions, the design values decrease slightly under the representative concentration pathways (RCP)2.6 and RCP4.5 concentration path for the low frequencies (exceedance probability of 0.1% and 0.2%), with increases for other frequencies (Yan et al. 2017). They increase under the RCP8.5 concentration paths for all, especially medium, frequencies (exceedance probability of 5% and 10%). By combining the time-varying moment model with the equal reliability method, more reliable design flood values can be obtained.

Practical Applications

Global climate change is the main driving factor influencing the hydrological cycle and water resource allocation. The Yalong River Basin is an important hydropower base in China, with a total planned installed capacity of 30 million kilowatts, raising the question of whether the cascade reservoirs will be operated safely under the dispatching rules set based on the historical flow regime. In this study, the authors analyzed and evaluated the situation of flood defense at the selected representative station. The authors developed a new method to cope with the challenge of flood defense risk evaluation under future climate change conditions based on special methods and principles. The results indicate that the frequency of floods in the Yalong River basin will increase owing to future climate change, leading to a greater risk on flood control. Therefore, a modification on the operation and management rules of cascade hydropower stations in the basin is unavoidable.

A study is presented for the assessment of different stormwater green infrastructure (SGI) for sustainable stormwater management, both stand-alone and in combination, in the rapidly developing city of Gurugram, India. A decision-making framework was developed for ranking the SGI based on five criteria (social, economic, environmental, technical, and legal and political) encompassing two quantitative and 12 qualitative subcriteria. The assessment of the application of SGI was carried out using Storm Water Management Model (SWMM) software and an expert survey involving participants from varied areas of expertise. The survey outcome determined the criteria weight by employing the analytic hierarchy process method, where technical and social criteria were given the highest (0.37) and the lowest (0.11) weight, respectively. Further, peak flow reduction potential was the most favored subcriteria (82%), while aesthetics was the least favored (58%). The prioritization of a set of predefined alternatives was carried out using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Results indicated that the relative closeness coefficient value for a combination of SGI, vegetated swales plus permeable pavements was 0.93, whereas for green roofs plus permeable pavements it was 0.84, and these were the most efficient alternatives to mitigate urban flooding in the city. The proposed SWMM-based TOPSIS approach can be helpful to decision makers and government bodies in the selection of SGI for urban stormwater management.

Inflow design flood (IDF) is selected following certain guidelines or criteria for hydrologic dam design. Fundamental to the development of these guidelines is the classification of dams and consequences of failure thereof. The guidelines for the selection of an IDF for a dam may vary from one country to another and from state to state within the same country. The objective of selecting an appropriate IDF is to achieve dam safety and reduce the risk of dam failure and mitigate consequences of failure. Measures of safety, risk, and consequences are not uniform across the world. The objective of this study therefore is to provide an international perspective of the guidelines employed for the selection of IDF for dam design.

The seasonal design flood (SDF) plays an important role in guiding water resources management and floodwater utilization. To estimate the SDF, studies have established the joint distribution of flood peak (i.e., annual maximum daily discharge) and timing (i.e., occurrence date) and assumed a monotonic dependence structure (MDS) between these two flood elements within the bivariate framework. However, the MDS assumption cannot fully represent the flood seasonality dynamics, which might affect the reliable assessments of the bivariate flood risks. To solve this issue, this study proposes a mixed-copula function to model a nonmonotonic dependence structure (NMDS) between flood peak and timing, and then derives the SDFs under nonstationary conditions by using the equivalent reliability method. As the NMDS can characterize the turnaround relationship between peak discharge and timing, the NMDS performs better at modeling observed (1951–2019) flood peaks than the traditional MDS, as reported by a case study in the Wujiang River Basin, China. The results suggest that, within the flood season from May to October, the NMDS-based estimation framework produces higher SDFs in June and July but smaller SDFs in the remaining months than the annual design flood. This allows for reducing the flood control storage capacity to improve floodwater utilization downstream without increasing flood risk. After further investigating the potential effects of a changing environment on peak discharges in the future (2020–2099), it is expected that reservoir regulation might play a more important role in flattening the seasonal fluctuations in floods. These results provide rich information as references for flood risk management and floodwater resources utilization under a changing environment.

Abstract

Groundwater flow velocity, including its magnitude and direction, is a crucial aquifer characteristic that plays a significant role in developing and protecting groundwater resources. Current groundwater measurement technologies yield low flow velocity accuracy and are expensive. This study proposes a new method for in situ measurement of groundwater velocity based on groundwater flow–induced cooling. It utilizes the cooling capability of flowing groundwater on a closed circulation of heated fluid to establish a functional relationship between the temperature difference of heated fluid and groundwater velocity. A series of experiments were conducted in a sandbox to verify this approach. The result shows that the new method can measure the groundwater velocity’s magnitude and direction. Within the designed flow rate range ($0-1.055\mathrm{mm}/\mathrm{s}$), the correlation coefficient between the calculated values obtained by the new method and the actual values exceeds 0.97, with only a 0.017 root mean square error and a 0.040 mean absolute error. Further, the method finds a significant sine function relationship between the temperature difference of heated fluid and the direction of groundwater flow, with the correlation coefficient exceeding 0.97.

Practical Applications

Accurately detecting groundwater flow velocity, including its magnitude and direction, plays a significant role in developing and protecting groundwater resources. However, traditional methods for measuring groundwater flow velocity, such as isotope tracing and borehole dilution tests, have a problem: measurement accuracy is not high, measuring equipment is expensive, and there is a long measurement time. Thus, we propose a fast and accurate method for measuring groundwater velocity. The new method utilizes the cooling capability of flowing groundwater on a closed circulation of heated fluid. A theoretical analysis of the method yields a mathematical relationship between the temperature difference of heated fluid and groundwater velocity. Based on the theoretical analysis, we designed and tested a series of experiments in a sandbox. The result shows that the new method can simultaneously measure the groundwater velocity’s magnitude and direction and has the advantages of high accuracy and strong robustness, which provides a theoretical reference for in situ measurement of groundwater velocity.

Abstract

A new single-site monthly streamflow simulation method that combines the maximum entropy and mixed copula methods is proposed. In this method, the marginal distribution of monthly streamflow is estimated using the maximum entropy theory; this is achieved by the conjugate gradient method of superlinear convergence and low computational expense. Then, a mixed copula is used for the construction of joint distributions of the adjacent monthly streamflow. The developed method is applied to four stations of the Weihe River, China; the results show that the important statistical characteristics of monthly streamflow can be maintained by the entropy-based marginal distribution and that the complex linear and nonlinear dependence between adjacent monthly streamflow can be reproduced by the mixed copula method. Compared with the traditional marginal distribution and the individual copula joint distribution, the entropy-mixed copula method has obvious advantages in goodness-of-fit statistical tests on the marginal and joint distributions.

Practical Applications

Accurately understanding the monthly runoff distribution is beneficial for flood control during the flood season and solving water shortage problems during the dry season. First, the marginal probability distribution function of each month is established through the principle of maximum entropy. Then, a mixed copula model is used to construct a joint distribution of adjacent monthly runoff. Finally, Gibbs sampling is used to obtain the randomly simulated runoff for each month. The advantage of this method is that it does not require assuming the probability distribution of the runoff series but rather uses important statistical features of the historical series as constraints for deriving the marginal distribution. Accurately describing the complex relationship between water flow sequences, maintaining the statistical characteristics of observation sequences, and improving simulation accuracy are of great significance for water resource planning and management.

Low-impact development practices (LIDs) have been used to reduce the impacts of flash floods in Brazil. However, there is a lack of investigations on their construction costs concerning the effects on the mitigation of flash floods. This paper addresses the construction costs and the runoff reduction by using seven scenarios of LIDs for a ${32\text{-}\mathrm{km}}^2$ basin in the Midwest Brazil region. The LIDs considered were permeable pavement (PP), rainwater harvesting (RWH), and infiltration trenches (IT). The first three LID scenarios considered PP, RWH, and IT techniques, respectively, and the other four used different combinations of the three LIDs. Scenario 3 showed a decrease of 15% in the runoff, achieved at a lower cost using only IT. This result corresponds to a 2.6% runoff reduction per $1 million spent. These analyses may help in the identification of the balance between benefit and cost by local stakeholders for the use of LIDs.

Uncertainty analysis is crucial for the quality control and application of hydrological models in water resources management. For improving the reliability of model outcomes, the identification of uncertainties originating from the knowledge gap and stochastic behavior of the model is required. To demonstrate a detailed investigation of uncertainty in hydrological model prediction, we developed a Hydrological Simulation Program-FORTRAN (HSPF) model for simulating streamflow at the South Chickamauga Creek watershed, Tennessee, and quantified uncertainty in the model outputs resulting from the input parameters using Monte Carlo simulation. Additionally, we compared the relative efficiency of Latin hypercube (LH) and Monte Carlo (MC) methods used for parameter distribution for the Monte Carlo simulation. Both methods produced similar results, where annual average runoff had the lowest, peak flow had the medium, and low flow had the highest level of uncertainty. Notably, our investigation revealed that extreme flow conditions exhibited higher uncertainty in prediction. Findings from this study will help in better interpretation of model results to devise more informed watershed management decisions.

Ground precipitation measurements face obstacles in many regions of Egypt, where gauge stations are sparse. It is thus necessary to find reliable sources such as satellite-based precipitation products, which provide uninterrupted precipitation time-series with high spatial resolution. This work evaluated the performance of four well-known global satellite precipitation products, i.e., Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM3B42V7), Integrated Multi-satellitE Retrievals for global precipitation measurement-Final (IMERG-F), and Global Satellite Mapping of Precipitation (GSMaP)-Gauge, against gauged data of 23 stations in Egypt. GSMaP-Gauge revealed its outstanding abilities over the other three products in detecting rainfall occurrences and estimating the amount of rainfall. Further, Global Satellite Mapping of Precipitation (GSMaP-Gauge) was corrected by three commonly used bias-correction methods: linear scaling; local intensity scaling; and empirical quantile mapping (EQM). All three methods are effective in reducing biases to some extent, especially on the annual scale, with EQM being the best for most cases. This study demonstrates that satellite products can provide an alternative source of rainfall information for Egypt.

Flash flooding is among the most hazardous natural events in the US and globally. Hydrologic simulations are key tools for flash flood forecasting, particularly in poorly gauged or ungauged catchments, and infiltration models are a crucial component of any hydrologic simulation. Ideally, model parameters are calibrated based on observations. In practice, calibration and validation are often not possible due to a lack of observations. This paper contributes new guidance for parameter estimation of three infiltration models using plot-scale rainfall simulation. All three models, namely, initial-constant (IC), linear-constant (LC), and the Green-Ampt (GA) models, will be available in the next release of the widely used Hydrologic Engineering Center–Hydrologic Modeling System software. Results show that model parameters are sensitive to soil texture, antecedent moisture conditions, and the presence of a physical soil crust.