Journal of Hydrologic Engineering

The Tigris and Euphrates basin is a challenging watershed because its water resources are critical for five countries. Therefore, this research analyzed and estimated the low flow index and its risk of occurrence. Based on 30 unregulated stations in the upper Tigris and Euphrates basin, different low flow indexes were estimated. A multivariate and hybrid principle component regression (PCR) regional model was used to assign the relationship between basin characteristics and low flow indexes in the homogeneous regions. The model performance showed that the principle component regression outperformed the regression models. The jackknife procedure indicated that the model performance for short-duration low flows is weak, but it is relatively good for long-duration low flows. This study suggests that hydrologic drought of the Tigris and Euphrates basin should be investigated more carefully for better water resources management in the changing socio-environmental system of the Middle East.

Scale dependence of dispersion coefficient ($D$) in the advection-dispersion equation (ADE) for solute transport in porous media was investigated by a series of experiments using image analysis. A hexahedral plexiglass box sized $200\times 8\times 1.5\mathrm{cm}$ ($\mathrm{L}\times \mathrm{W}\times \mathrm{H}$) was set and packed with glass beads as porous media. The solute transport under different conditions was simulated by changing the particle size of glass beads, flow rate, and detection scale using Bright Blue as tracer. The image analysis method was used to dynamically monitor and identify the spatiotemporal variation of solute concentration distribution. The results showed that image analysis can effectively monitor and identify the solute concentration in porous media, as indicated by an $R^2$ value of 0.9890. There is an obvious linear relationship between hydraulic gradient ($J$) and velocity ($v$) in porous media under different experimental conditions. The ADE model is suitable for solute breakthrough curve (BTC) with good fitting accuracy, and can effectively reflect the concentration variation during solute transport. The key parameters controlling the solute transport were analyzed. $D$ has abnormal diffusion (i.e., non-Fickian phenomenon) and scale dependence, and BTCs had a long tail, which becomes more obvious with the increase of flow rate, medium particle size, and transport scale.

This study calibrated and downscaled data from four scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6) to drive the distributed hydrological model soil and water assessment tool (SWAT). Hereafter, this study systematically quantified the changes in streamflow under future climatic conditions in the middle-upper reaches of the Weihe River Basin. The results show that, compared with the base period (1991–2000), the low-emission SSP1-2.6 scenarios resulted in future streamflow reductions to values less than those recorded in the baseline period. Furthermore, the same is true for medium and high-emission SSP3-7.0 scenarios. Under the SSP1-2.6 emission scenarios, streamflow in the 2030s, 2040s, 2050s and 2060s will be reduced by 40%, 50%, 31%, and 38%, respectively. Under the SSP3-7.0 emission 12 scenarios, streamflow will decrease by 44%, 20%, 34%, and 7% in the 2023s, 2040s, 2050s and 2060s, separately. These results supply support for water resource management and water use in the Kuan-chung Plain in the future climate change.

Areal reduction factors (ARFs) are widely used to transform the point rainfall intensity to the areal rainfall intensity in engineering practice. Inappropriate ARFs may result in an overestimate or underestimate of the areal rainfall and consequently lead to the inappropriate design of infrastructure. This study aims to explore the differences in ARFs estimated by four empirical methods and quantitatively analyze the effect of rainfall duration, area, return period, local topography, and rain gauge density on ARFs in the coastal city Shenzhen, China. The results indicate that the original fixed-area method yields more conservative (higher) ARF estimates than the other three methods, which also consider the return period with the coefficient of variation ranging from 0.014 to 0.054. Bell’s method and its modified versions produced modest discrepancies in ARFs, with coefficient of variation (COV) values ranging from 0.008 to 0.023. A declining trend of ARFs with increasing return period was observed for six durations (1, 2, 3, 6, 36, and 48 h), whereas ARFs tended to increase with increasing return period for 12- and 24-h durations. Meanwhile, ARFs in mountainous areas (the east part of Shenzhen) were lower than that in the flat terrain in the west part with a maximum reduction of 0.13, which might be associated with the higher spatial variability of rainfall caused by the terrain effect. In addition, ARFs derived from the sparse rain gauge network may be overestimated compared with that from the dense network (maximum overestimation of 0.041). This study provides new insights into the relationship between ARFs and return periods, and highlights that ARFs should be further studied based on the up-to-date rainfall data to tackle the changing climate.