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.

Drought and water scarcity are key aspects in the management of groundwater resources in regions with excessive withdrawal. In this paper, groundwater drought, water scarcity, and their compound impact were analyzed based on a new evaluation index in overdrafted aquifers. Groundwater drought analysis was performed using the standardized groundwater index (SGI) on the naturalized groundwater level time series. For this purpose, the MODFLOW groundwater simulation tool and a surrogate artificial neural network model were adopted to obtain the time series of the naturalized groundwater level in Qazvin plain, central Iran, over the 1966–2016 period. Moreover, a water scarcity index, namely the deficit rate (DR), and a droughtwater scarcity (DWS) index were introduced. Results showed that abstraction could cause a severe negative trend in the groundwater level in comparison with natural causes (i.e., drought). Furthermore, use of the DWS index revealed that the safe yield of the Qazvin aquifer was about 44% of the existing abstraction volume. It is concluded that the DWS index represents a comprehensive criterion in water resource assessment and groundwater safe yield computations.

A robust hierarchical operational testing scheme has been adopted to test the credibility of a newly developed large-scale conceptual hydrological model in the Subarnarekha basin, India. We have evaluated the results using $M$ [combining Nash-Sutcliffe efficiency (NSE), and $R^2$], $p_{PBIAS}$-, and $p_{\mathrm{RMSE}}$-values [combining the jackknife method and analysis of variance (ANOVA)] along with root-mean square error (RMSE), NSE, $R^2$, and percent bias (PBIAS). Results show that the model has passed various steps of the testing scheme successfully, and is both geographically and climatically transposable. The results of a proxy-basin test [transferring parameters into Ghatshila subbasin after calibration in Muri and crossvalidation in Jamshedpur ($M=0.62$)] show satisfactory RMSE ($271.14{\mathrm{m}}^3/\mathrm{s}$), NSE (0.42), $R^2$ (0.45), PBIAS (30.22%), and statistically significant $p_{\mathrm{RMSE}}$-value (0.043) and $p_{PBIAS}$-value (0.002) to confirm the model as geographically transposable. Satisfactory RMSE ($9.71{\mathrm{m}}^3/\mathrm{s}$), NSE (0.66), $R^2$ (0.84), and PBIAS (1.66%) for dry year validation under differential split-sample test in Muri subbasin confirms the model as climatically transposable. The uncertainty analysis, using quantile regression technique, represents reasonable predictive ranges, e.g., for Muri calibration and validation 95 percent prediction uncertainty (PPU) band encloses 63% and 76% of the observations.

Efforts under stormwater best management practices have led to low-impact development (LID) concepts for innovative stormwater designs. The key issue involved in the revolution of stormwater management from quantity to quality control has been the selection of the design event. Among many innovative approaches, the concept of runoff capture has been widely accepted and evaluated with data analyses and mathematical models. In this paper, both runoff event and volume capture methods are derived using the exponential model and solved for a selected runoff capture rate. Both methods provide identical assessment of the runoff capture rate for a selected basin storage volume. Both methods provide the identical water quality capture volume (WQCV) when the runoff capture rate is set to be the 80th percentile of either rainfall or runoff population. Two design charts, one for the US continent and the other for mainland China, are produced, respectively, using the runoff capture methods. This paper provides examples using long-term continuous rainfall data recorded at the City of Denver, Colorado and the City of Beijing, China to illustrate how to apply the empirical approach, theoretical methods, design charts, and guidance on the selection of storage volumes for stormwater LID designs.

The forms of the intensity-duration-frequency (IDF) function that best fit rainfall data in the United States are investigated. Analyses using data from 9,264 rainfall stations in the National Oceanic and Atmospheric Administration Atlas 14 database show that, for any given return period, the three-parameter Sherman and Kimijima equations give the best performance, with average relative errors typically less than 4%, although at some locations, errors are significantly higher. Geographic regions of the United States and their associated best-fitting IDF functional forms are identified. The performance of IDF functions that include both duration and return period are also analyzed, and the results show that four-parameter IDF functions derived from the Sherman and Kimijima equations perform best, although the best-performing function has a regional dependence.

Modeling the simultaneous behavior of flood characteristics, namely peak, volume, and duration, is essential for water resource planning and management. A multivariate probability approach, which provides a comprehensive understanding of flood characteristics and their relationship, may estimate flood magnitude more accurately than a univariate approach. Most previous studies related to the multivariate frequency analysis of extreme events assumed temporal stationarity. However, several recent studies show that flood characteristics exhibit nonstationary behavior due to climate change, urbanization, land-use change, or water resource structures. Therefore, it is necessary to perform multivariate frequency analysis in a nonstationary condition. In this study, nonstationary bivariate models, where the parameters of the marginal distribution vary with possible physical covariates (i.e., precipitation, urbanization, and deforestation), are developed to understand/model the nonstationary behavior of the flood characteristics of the Dongnai River in Vietnam. This study indicates that the assumption of temporal stationarity in flood characteristics leads to an underestimation of flood risk. For example, the flood characteristics’ quantiles estimated for a 50-year return period in a stationary condition is nearly equal to flood characteristics’ quantiles estimated for a 10-year return period in a nonstationary condition. Specifically, the volume and peak pair calculated for a nonstationary condition for the 10-year joint return period (OR) is (625.8, 147.8). The volume and peak pair calculated for a stationary condition for the 50-year joint return period (OR) is (620.1, 148.8).

Most of the studies in humid catchments have discovered a similar conclusion that runoff generation was dominated by overland flow and interflow. However, the Hemuqiao watershed with long-term hydrological data which is located in the lower reach of Yangtze River shows a different runoff generation mechanism by comparing different tracers on the hydrograph separation analysis. The mechanisms of the runoff generation process from rainfall to streams during the typhoon events are studied by using a mixed method in the Hemuqiao watershed. The oxygen-18 ($\delta {}^{18}\mathrm{O}$) isotope ratios and electrical conductivity (EC) of precipitation in four storm events are significantly different from those in the river and the baseflow, allowing two-component hydrological separation to determine the contribution of the event and pre-event water. Both EC and stable isotopes give qualitatively similar results that the pre-event water component accounts for the majority of the hydrograph during both typhoon events ($\ge 60\%$). This is primarily affected by rain intensity and the antecedent moisture condition. The EC method may slightly overestimate the proportion of pre-event water than the $\delta {}^{18}\mathrm{O}$ method. This may be due to the different factors that affect the variation of EC and stable isotopes and influenced by neglecting the soil water recharge to the stream. The value of the isotopic composition of streamflow and surface water are generally distributed along the local meteoric water line, which indicates that the main source of water in the Hemuqiao watershed is supplied by precipitation. The EC value of water samples collected from precipitation, streamflow, surface water, soil water, and groundwater is shown to be different because of the water–soil contact time and characteristics of soil layers. According to isotopic–hydrochemical tracer analysis, we clearly see that the water which is stored in soil and groundwater controls the value, lag time, and the composition of EC and isotopic of the streamflow in typhoon events.

A danger level at a river location is the level above which a flood may cause loss and damage to nearby lives, crops, and homesteads. Currently, in Bangladesh, the danger level is designed and updated considering the riverine flood during the monsoon season (June–September). However, during the premonsoon season (March–May), flash floods, almost every year, damage crops in some parts of the northeast Haor (saucer- or bowl-shaped large tectonic depression) region. Damage to Boro rice, the only crop that is grown in this region, is catastrophic to the local population and threatens the food security of the country. At present, there is no warning or danger level to inform the community to save the Boro crops from flash floods in that region. Hence, this study attempts to establish a flash flood danger level at 40 locations in the northeast region of Bangladesh based on flood frequency analysis and elevations of the river floodplains. The homogeneity and adequacy of the data series was tested by performing the run test, lag 1 auto correlation, Mann-Kendall test, and Mann-Whitney test. After passing the data quality tests, flood frequency analysis was conducted using six widely used distributions: lognormal, lognormal Type III, Pearson Type III, log-Pearson Type III, and Gumbel and generalized extreme value (GEV) distributions. Best regional distribution was selected from the probability plot correlations coefficient (PPCC), normalized root mean square error (NRMSE), and mean absolute error (MAE) results. Among the candidate distributions, GEV distribution was found to provide the best results. Then, the danger level was fixed on the basis of the flooding inundation scenarios of different return periods at which the stage is at or near the floodplain level. The results showed that for most of the stations, the 2.33-year flood level coincides with the existing floodplain level. For the rest of the stations, flash flood danger levels were fixed at the 5-year return period’s flood level. The recommended flash flood danger levels were found much lower than the existing monsoon flood danger levels. For example, at Sunamganj station, the existing flood danger level is 8.25 mPWD (Public Works Datum) whereas the recommended flash flood danger level is only 6.41 mPWD. At some stations, the difference is too prominent, even more than 5 m, which can cause severe destruction for the Boro rice cultivation during a flash flood. It is expected that flash flood danger levels estimated in this study may assist in flash flood forecasting in the northeast Haor region and provide an effective warning at the time of necessity, thus minimizing the loss and damage by the flash flood to some extent.

Probable maximum precipitation (PMP) is the primary criterion used to design flood protection measures for critical infrastructures such as dams and nuclear power plants. Based on our analysis using the Stage IV (ST4) quantitative precipitation estimates, precipitation associated with Hurricane Harvey near Houston, Texas, represents a PMP-scale storm and partially exceeds the Hydrometeorological Report No. 51 (HMR51) 72-h PMP estimates at $\mathrm{5,000}{\mathrm{mi}}^2$ ($\mathrm{ST}4=805\mathrm{mm}$; $\mathrm{HMR}51=780\mathrm{mm}$) and $\mathrm{10,000}{\mathrm{mi}}^2$ ($\mathrm{ST}4=686\mathrm{mm}$; $\mathrm{HMR}51=673\mathrm{mm}$). We also find statistically significant increasing trends since 1949 in the annual maximum total precipitable water and dew point temperature observations along the US Gulf Coast region, suggesting that, if the trend continues, the theoretical upper bound of PMP could be even larger. Our analysis of Hurricane Harvey rainfall data demonstrates that an extremely large PMP-scale storm is physically possible and that PMP estimates should not be considered overly conservative. This case study highlights the need for improved PMP estimation methodologies to account for long-term trends and to ensure the safety of our critical infrastructures.