Journal of Hydrologic Engineering

This is the first study to assess the possible changes in floods in the Bangladesh part of the densely populated Ganges–Brahmaputra–Meghna (GBM) delta at 1.5°C, 2°C, and 4°C global warming levels. This study was undertaken with the aim of joining the efforts of the global scientific community to assist in the preparation of the upcoming Special Report on 1.5 Degrees by the Intergovernmental Panel on Climate Change. The future changes in the possibilities of peak synchronization of nearby large rivers were assessed for the first time. Peak synchronization is critical for flood assessment in low-lying delta regions like Bangladesh. Results indicate that the flood peaks of the GBM rivers are more likely to synchronize in the future. Results also indicate that the flood magnitudes may become more severe in the future. At global warming levels of 1.5°C, 2°C, and 4°C, flood flows with a 100-year return period are projected to increase by about 27%, 29%, and 54% for the Ganges; 8%, 24%, and 63% for the Brahmaputra; and 15%, 38%, and 81% for the Meghna, respectively, compared with a baseline of 1986–2005.

This study assesses the advantages and limitations of developing design storms using the partial duration series exceedance-based (PDS-E) rainfall frequency model in comparison with the annual maximum series (AMS) model. Rainfall records were obtained from 21 rainfall gauges in Ontario. A procedure to develop the PDS-E was derived, addressing sensitivities to missing values, selection of thresholds, and quantile and confidence limit estimates. The reference values of 1-h duration and 2- and 5-year return period design rainfall intensities were 10% and 3% greater in PDS-E than in AMS data, on average, indicating the merits of using PDS-E data for exceedance-based modeling of frequent events. The PDS-E estimates of 1-h duration and 5-year return period events were 3.5% greater than AMS estimates. The use of the PDS-E method over the AMS method for developing design storms for stormwater infrastructure is recommended.

To solve the low-precision problem of traditional methods for groundwater depth prediction, a nonlinear prediction model based on empirical mode decomposition (EMD), phase space reconstruction (PSR), particle swarm optimization (PSO), and extreme learning machine (ELM) was proposed to predict the groundwater depth at Friendship Farm in Heilongjiang Province, China. In this study, the original time series of groundwater depth was preprocessed (decomposed and reconstructed) using EMD and PSR, and then different PSO-ELM models were constructed for the prediction of groundwater depth. The results indicated that the models had a good prediction effect and estimated the following indicators well: the posterior error ratio ($C$), small error frequency ($p$), relative mean square error ($E_1$), fitting accuracy ratio ($E_2$), and test forecast effect index ($E_3$). Comparison of PSR-ELM, PSR-PSO-ELM, and EMD-PSR-PSO-ELM showed a good agreement of root mean square error (RMSE). The results exhibited that the RMSE of PSR-ELM and EMD-PSR-PSO-ELM reduced from 0.4965 to 0.1694 m, and that of PSR-PSO-ELM and EMD-PSR-PSO-ELM reduced from 0.3418 to 0.1694 m, respectively. The results also showed that EMD and PSO effectively improved the prediction performance of the ELM model. This paper also analyzes the effects of climatic factors and human activities on the dynamic changes of local groundwater depth. The results suggest that the effect of precipitation and agricultural production mainly reflected the dynamic groundwater depth.

Effective load reduction strategies rely on an accurate Total Maximum Daily Load (TMDL) calculation, which assesses pollutant loading from various sources. Quantification of uncertainty is a critical part of the TMDL process. To account for uncertainty, this paper adapts the load resistance factor design (LRFD), a reliability-based framework, to water quality assessment and the TMDL process. The LRFD replaces the traditional lumped margin of safety (MOS) with design factors that reflect the magnitude and distribution of uncertainty among the various contaminant loads. In addition, it produces load reduction estimates to meet management objectives with a contaminant-specific frequency-based target. The LRFD is computationally efficient and flexible in that, to compute the design factors, the procedure can use measurement data, analytical solutions or model simulation results, as well as full or marginal probability distributions. The applicability of the proposed approach is demonstrated with two case studies.

In this study, new methods based on integrating discrete wavelet transforms (DWT) into artificial neural network (ANN), gene expression programming (GEP), and decision tree (DP) approaches for several applications of water quality index estimation are proposed. The 3-year daily data used in this study, including turbidity (Tur), pH, dissolved oxygen (DO), discharge, and temperature, were measured from the Blue River at Kenneth Road, Overland Park, Kansas, in Johnson County. In addition to the time delays concerning each of the parameters DO, Tur, and pH, the temperature and discharge were considered effective in the climate of the region. The results showed that using wavelets significantly improved the performance of the ANN, DT, and GEP models, particularly in the case of extreme values. The results are comparable and suggest that wavelet-AI conjunction models could be explored as an alternative tool for water quality prediction. The performance of wavelet-gene expression programming (WGEP), which was moderately better than wavelet-artificial neural network (WANN) and wavelet-decision tree (WDT), is very promising and hence supports the use of WGEP in predicting river quality data. The results showed that the WGEP model decreased the mean absolute percentage error for the WDT, WANN, GEP, ANN, and DT models from 0.18, 0.33, 0.41, 0.47, and $0.35\mathrm{mg}/\mathrm{L}$, respectively, to $0.17\mathrm{mg}/\mathrm{L}$ for the DO index, and from 0.026, 0.018, 0.036, 0.08, and $0.051\mathrm{mg}/\mathrm{L}$ to $0.04\mathrm{mg}/\mathrm{L}$ for the pH index, respectively. The WANN model also dropped the mean absolute percentage error for the WDT, WGEP, GEP, ANN, and DT models from 9.72, 6.68, 13.98, 8.88, and 14.81 FNU to 5.06 FNU for the Tur index. In this study, hybrid models provided more precise predictions for extremely high values.

Taking into account the climatic conditions of the semiarid region of Brazil, with its intermittent rivers and long periods of water scarcity, a dense network of surface reservoirs (on average one dam every $5{\mathrm{km}}^2$) of very different sizes has been built. The impact of such a network on water and sediment dynamics constitutes a remarkable challenge for hydrologists. The main objective of this work is to present a novel way of simulating water and sediment fluxes through such high-density reservoir networks, which enables the assessment of water and sediment retention in those structures. The new reservoir modeling approach has been coupled with the fully process-oriented and semidistributed hydrological WASA-SED model, which was tailored for semiarid hydroclimatological characteristics. This integrated modeling system was applied to the $933\text{-}{\mathrm{km}}^2$ Benguê catchment, located in semiarid northeastern Brazil, which has a network of 114 reservoirs with a wide range of surface areas (from 0.003 to 350 ha). The small reservoirs were grouped into size classes according to their storage capacity and a cascade routing scheme was applied to describe the upstream-downstream position of the classes; the large reservoirs were handled explicitly in the reservoir modeling approach. According to the model results, the proposed approach is capable of representing the water and sediment fluxes though the entire reservoir network with reasonable accuracy. In addition, the model shows that the dynamics of water and sediment within the Benguê catchment are strongly impacted by the presence of multiple reservoirs, which are able to retain approximately 21% of the generated runoff and almost 42% of the sediment yield of the catchment for the simulation period, from 2000 to 2012.

This paper presents a procedure to derive historical-annual and historical-monthly flow duration curves (FDC) that are monotonic and continuous for agricultural, unregulated, ungauged sites. The authors explore the performance and the regional dependence of four different regression models for the estimation of daily flow quantiles ($Q_p$), with probabilities of exceedance ($p$) ranging from 0.01 to 0.99. The construction and analysis of the four regression models is based on scale-invariance of $Q_p$ with respect to drainage area ($A$) or mean annual flow ($\overline{Q}$). The authors implemented the procedure using data from 74 gauged sites in the state of Iowa. The study concludes that the multi-scaling framework of $Q_p$ with respect to $\overline{Q}$ provides the most accurate FDC estimates, which are region-independent within the physical characteristics of Iowa. The authors developed a piecewise continuous function to estimate the scaling parameters as a function of $p$, allowing estimations of continuous FDCs using independent estimates of $\overline{Q}$. A validation test using data from 40 gauges not employed in developing the regression equations shows that the methodology performs similarly for historical-annual FDC estimates when compared to that developed for Iowa by the US Geological Survey. For discharges with $p$ values larger than 0.6, the proposed methodology exhibits lower, but still acceptable, performance. Two features make the proposed methodology attractive for FDC estimation: first, it is simpler to use and implement than the USGS methodology because it only requires estimates of $\overline{Q}$; and second, it provides reliable FDC estimates at monthly scales, which are otherwise unavailable. The authors present an application example in which FDC estimates at ungauged sites are used to create maps of streamflow frequency to categorize flows predicted in real time by a distributed hydrologic model implemented for Iowa.

The main purpose of this study is to define the most effective periodic components (modes) that produce the trends in 7-day annual minimum flows (AM7) in two important river basins in Turkey, namely, Euphrates and Çoruh. To achieve this aim, additive wavelet transform (AWT) in conjunction with the Mann-Kendall (MK) and the sequential MK tests is applied for the first time. According to the MK test, significant decreasing trends were detected for the AM7 series of the stations located in the middle and lower parts of the Euphrates Basin and in the upper part of the Çoruh Basin. Moreover, the MK test showed significant increasing trends for the AM7 series at the stations located in upper part of the Euphrates Basin. No significant trend was detected for the AM7 series of the stations in the lower and middle parts of the Çoruh Basin. Moreover, the sequential MK test applied to the AM7 series showed that the detected decreasing trends become significant after 1988, whereas the increasing trends become significant after 2007, and these significant trends continue during the present time. Then, by using AWT, the original AM7 series at each station were decomposed into their approximation and wavelet subbands, representing 2, 4, 8, and 16 yearly time series. Furthermore, by using AWT coupled with the MK and sequential MK tests, it was found that the 2- and 4-year periodic components are the most dominant modes that produce trends in the original data series of the Euphrates and Çoruh Basins, respectively. The results demonstrated that the AWT-based trend approach can be successively applied for defining the dominant modes in the AM7 series and effectively used in any hydroclimatic time series cases.

While excessive sediment is a leading cause of aquatic life use impairments in free-flowing rivers in Virginia, there is no numeric sediment-water quality criterion. As a result, total maximum daily load (TMDL) sediment loads are often established using a comparable, nonimpaired reference watershed. Selecting a suitable reference watershed can be problematic. This case study compared the reference watershed approach (RWA) which uses the Generalized Watershed Loading Function and the disaggregate method (DM) which uses output from Phase 5.3 of the Chesapeake Bay Watershed Model. In this case study, the two methods were used to develop sediment TMDLs for three impaired watersheds in Virginia (Taylor Creek, Turley Creek, and Long Meadow Run). In this case study comparison, the RWA required between 12.8 and 14.7 times greater sediment load reductions ($\mathrm{t}/\mathrm{year}$) to reach the TMDL load (Taylor Creek > Long Meadow Run > Turley Creek) when compared to the reductions called for using the DM. While each TMDL development method has inherent limitations, the DM uses output from the Chesapeake Bay Watershed Model to establish TMDL target loads. This means that the application of the DM is restricted to the Chesapeake Bay Watershed.

The hydrological processes of the middle-lower reaches of the Hanjiang River have been altered significantly because of water diversion and reservoir construction. The combined wavelet transformed Mann-Kendall (WT-MK) trend test was used to improve detection of the variability of streamflow and aberrance points. The indicators of hydrologic alteration (IHA), the Dundee hydrological regime alteration method (DHRAM), the ecosurplus (ES) and ecodeficit (ED), and the double mass curve (DMC) methods were applied to reveal hydrologic alterations associated with reservoir construction from multiple perspectives. Results indicated that (1) the integrated alteration degrees at Huangjiagang, Huangzhuang, and Shayang stations were 35.94, 32.46, and 36.35, respectively, and there were two, one, and one indicators (high level) at the three respective stations; (2) both an annual and seasonal ED occurred, whereby the magnitude of high flows decreased and the magnitude of low flows increased, which led to the ED in normal periods and the ES in dry periods becoming more apparent; and (3) the hydrologic regime was affected by human activities from 1975 to 2009, especially from 2000 to 2009.

This study presents a probabilistic framework that considers both the water quality improvement capability and reliability of alternative total maximum daily load (TMDL) pollutant allocations. Generalized likelihood uncertainty estimation and Markov chain Monte Carlo techniques were used to assess the relative uncertainty and reliability of two alternative TMDL pollutant allocations that were developed to address a fecal coliform (FC) bacteria impairment in a rural watershed in western Virginia. The allocation alternatives, developed using the Hydrological Simulation Program—FORTRAN, specified differing levels of FC bacteria reduction from different sources. While both allocations met the applicable water-quality criteria, the approved TMDL allocation called for less reduction in the FC source that produced the greatest uncertainty (cattle directly depositing feces in the stream), suggesting that it would be less reliable than the alternative, which called for a greater reduction from that same source. The approach presented in this paper illustrates a method to incorporate uncertainty assessment into TMDL development, thereby enabling stakeholders to engage in more informed decision making.