Journal of Hydrologic Engineering

Seasonal drying and wetting highly influence ecosystem diversity in intermittent streams. Hydrological alterations are responsible for the spawning and reproduction of aquatic habitats. The present study attempts to understand the impact of streamflow variations on the adult Bhangun fish (Labeo bata) in in Bhogdoi River, India, using eco-hydraulic modeling. Increased anthropogenic activities are constantly deteriorating the flow conditions and endangering the fish abundance in the reach. The eco-sustainability and habitat suitability of the target species are evaluated from the integrated hydrodynamic-habitat modeling under the various flow regimes. The ecological flow rates are obtained from flow duration curve analysis and flow duration curve shifting methods to indicate the hydrological changes under different environmental management scenarios. Two-dimensional depth-averaged shallow water equations are used to estimate the flow depth and current speed in the domain for different streamflow events. The hydrodynamic model is validated with the field observed data. Results of the study indicated that with the degradation in ecosystem protection, the environmental flow rates decrease. The hydrodynamic-habitat simulations indicate that the maximum flow and the ecological flow rate in the stream deprived the target species of a favorable environment to survive. The model outputs provide useful insight for ecosystem richness and diversity maintenance in the domain and help stakeholders in decision-making.

In the Green-Ampt model, the infiltration rate is indirectly related to time via the intermediate term of cumulative infiltration depth. In the present study, the direct theoretical relationship between the infiltration rate of the GA infiltration model with time is derived and elaborated. A new laboratory method for separate measurement of the GA model parameters is developed and tested via the constant-head infiltration tests on two sandy soil columns considering different values of initial soil water content. Hysteretic soil water characteristic (SWC) curves of studied soils are also measured. Results indicate that the GA model parameters are constant values and independent from the initial soil water content. Hydraulic conductivity $K$, wetting front matric head ${\mathrm{\Psi }}_{\mathrm{f}}$, and water content of wetted zone ${\theta }_{\mathrm{wz}}$ of GA model are separately estimated from proposed method and are found to be respectively equal to saturated hydraulic conductivity $K_{\mathrm{s}}$, water-entry value $h_{\mathrm{w}-\mathrm{e}}$, and saturated water content of the wetting SWC curve ${\theta }_{\mathrm{s}}^{\mathrm{w}}$. By setting $K=K_{\mathrm{s}}$, ${\mathrm{\Psi }}_{\mathrm{f}}=h_{\mathrm{w}-\mathrm{e}}$, and ${\theta }_{\mathrm{wz}}={\theta }_{\mathrm{s}}^{\mathrm{w}}$, the results of the GA model matched reasonably well with the numerical solution of Richards’ equation using HYDRUS-1D and measured data.

Climate change and human activity has increased the degree of nonstationarity in the annual runoff series of the Weihe River Basin, making it difficult to conduct a traditional hydrological frequency analysis in this study area. Most of the existing nonstationary frequency analysis methods are still controversial and cannot be applied in practice. Furthermore, the nonstationarity of the annual runoff series is caused by nonstationary changes in some specific influencing factors. In this study, we remove the nonstationarity from the annual runoff series by quantifying the relationship between the annual runoff and its driving factors. This denonstationarity process removes the nonstationary influence of the driving factors and is not limited to some specific types of nonstationarity, such as trend and abrupt change. For the annual runoff series of the Weihe River, the annual average temperature $(T_{ave})$, irrigation area $(IA)$, and reservoir index $(RI)$ are used as the reconstruction factors, respectively. And the decreasing trends in the first and second moments, and an abrupt change in 1993 are removed simultaneously by this denonstationarity method. The stationarity of the denonstationarity series reconstructed by $T_{ave}$ is the best, followed by the denonstationarity series reconstructed by the $IA$ and the $RI$. Climate change has a more significant impact than human activity on the nonstationary change of the annual runoff. Once the reconstructed series are stationary, the traditional hydrological frequency analysis method is adopted. Reconstructed series with a higher degree of stationarity result in lower uncertainties in the design quantile of the annual runoff, whereas the significant nonstationarity in the original series results in large design value uncertainties.

Groundwater contamination source characterization is the prerequisite for the rehabilitation and remediation of contaminated aquifers. This study demonstrates the computational feasibility and application of an efficient linked simulation-optimization based methodology for optimal characterization of multicomponent reactive contamination sources in a complex contaminated aquifer. The computational encumbrance of the complex reactive transport simulation is ameliorated using sufficiently accurate multioutput support vector regression (MSVR)–trained surrogate models as embedded constraints within the simulation-optimization framework. Three different metaheuristic optimization algorithms, i.e., genetic algorithm (GA), particle swarm optimization (PSO) , and generalized simulated annealing (GSA), were used, and their comparative performances were evaluated for a contaminated aquifer resembling a filed-scale contaminated mine site located in Northern Territory, Australia. The performance evaluation result based on an actual contaminated aquifer site along with synthetic concentration measurements indicated that the performance of the GSA-based source identification model is superior compared to PSO and GA. The reliability and robustness of the GSA model were further investigated for erroneous concentration measurement scenarios. The preliminary findings demonstrated the computational efficiency and accuracy of the proposed MSVR surrogate model–assisted optimization for simultaneous identification of multicomponent reactive sources at complex geochemically contaminated field-scale aquifers.

The Yellow River is the second largest river in China, providing precious water resources to ecosystems in northern China. The Sanmenxia and Xiaolangdi are two large cascade reservoirs in the middle Yellow River that have significantly altered the regional ecohydrological conditions. Multiple methods, including the sequential Mann–Kendall test, range of variation approach, and ecoflows based on flow duration curves, were used to assess the ecohydrological impacts of the two reservoirs. Results indicated that (1) in detecing impact years by the sequential Mann-Kendall test, the minimum daily discharge of each year performed better than the maximum, mean, and median daily discharges of earch year; (2) the delay in the date of minimum flow caused by the Sanmenxia Reservoir and the advance in the date of maximum flow caused by the Xiaolangdi Reservoir had a serious effect on some native fish species; (3) the integrated hydrological changes caused by the Sanmenxia Reservoir and Xiaolangdi Reservoir were 30.91% and 51.39%, respectively; (4) the Sanmenxia Reservoir amplified the annual ecodeficit, whereas the Xiaolangdi Reservoir reversed the annual ecosurplus from a decreasing to an increasing trend and the annual ecodeficit from an increasing to decreasing trend; and (5) the autumn ecoflows experienced the greatest impact, with accompanying deterioration, although the Xiaolangdi Reservoir reduced the increasing rate of autumn ecodeficit and radically improved the previous unfavorable conditions in other seasons. The results shed new light on the ecohydrological impacts of large dams and are beneficial for the restoration of the aquatic ecosystems in the lower Yellow River.

In this study, a method was developed for the baseline characterization of river bathymetry and time-varying heights using globally available datasets from the Shuttle Radar Topography Mission (SRTM) elevation data and Landsat visible imagery. Using independent data on river water elevations from satellite altimetry, the SRTM-Landsat approach was verified as to how well it can work for baseline characterization. The technique was demonstrated for Chindwin River locations in Myanmar that were also independently sampled by Sentinel 3A and Jason 3 altimeters. The Modified Normalized Difference Water Index (MNDWI) was used for estimating the water areas and widths using Landsat 8 from 2016 to 2019. A comparison of SRTM-Landsat with Sentinel 3A/Jason 3–based elevation changes resulted in a correlation coefficient up to 0.89 and 0.82 using area-elevation and width-elevation curves, respectively. The presence of river islands during the dry season resulted in a weaker correlation between our proposed SRTM-Landsat technique and altimeter water elevations. This case study over the Chindwin River in Myanmar demonstrated that the use of the SRTM-Landsat combined technique could yield an acceptable baseline for characterization of river bathymetry and time-varying heights at ungauged locations around the world.