Journal of Hydrologic Engineering

Rainfall-runoff conceptual models are used largely for river discharge prediction, for waterworks design, and as support for water quality assessment. The generalized split-sample test (GSST) recently was recommended to analyze rainfall-runoff models’ performance. Moreover, it was found that parameter transfer may be conditioned to the precipitation conditions of the donor and receiver periods. This study focused on the generalized split-sample test results, and analyzed them in terms of the information gain between rainfall and runoff series. This issue was not considered before in GSST interpretation. Six small to moderate-sized basins ($50–500{\mathrm{km}}^2$) in northern Tunisia were studied using the daily bucket with a bottom hole (BBH) model and the GSST calibration-validation approach. The mean absolute error and the Nash–Sutcliffe efficiency (NSE) were adopted to quantify model performance. The analysis suggests that the mean information gain (MIG) may be an indicator of the explored hydrological conditions of the assessment periods. In addition, results show that validation periods characterized by high MIG improved robustness, displaying low standard deviation of monthly NSE and enhanced accuracy, as shown by mean monthly NSE. The study of the effects of underlying physiographic factors suggests that the transfer from period to period is likely to be more robust for moderate-size basins than for small basins and that basin steepness tends to decrease the robustness of the transfer.

The relative importance of climate change and anthropogenic impacts on hydrology in the Hanjiang River Basin in China have become a hot topic because it has been manipulated by multiple activities, including reservoir regulation and interbasin water diversion that have abrupt impacts as well as land use change and irrigation that have gradual impacts. Using long-term data of streamflow, climatic variables, agricultural statistics, and land use maps during 1965–2017, the Mann-Kendall test was applied to detect hydroclimatic trends, and a Budyko-based covariate analysis was used to evaluate the individual and combined effects of climate variability and human activities on streamflow. Results indicated the following: (1) there was a significant increase of temperature, which may be human-induced to some extent; (2) the overall anthropogenic effect due to urbanization, reservoir regulation, increased irrigated areas, and loss of cultivated fields increased streamflow, which compensated the effects of climate change; (3) climate change was the dominant factor for streamflow decrease in the upper basin, while anthropogenic impacts in the middle basin overweighed climate change; and (4) an annual streamflow variation in the upper basin due to the regulation of the Danjiangkou Reservoir was nonignorable.

Regional models of flow duration curves (FDC) are ubiquitous components in water resources studies, particularly in ungauged sites or in catchments with scarce monitoring. The identification of such models in arid regions, in which intermittent and ephemeral streams are common, constitutes an additional challenge in statistical hydrology applications. In this paper, we propose an alternative approach for the modeling of FDCs in null-flow conditions. First, we evaluated a set of flexible distributions frequently used in hydrology for modeling the entire range of observed streamflows, with a focus on the proper representation of intermittence and the description of the lower flows. Next, regional models for the parameters of the distributions were identified with the evolutionary polynomial regression (EPR) technique, which, although providing structurally complex equations, proved able to uncover the relationships between the characteristics of such parameters and catchments and, more importantly, preserve them in a cross-validation procedure. From a practical perspective, the decision-making processes involving water resources planning and management could be more coherently addressed in ungauged sites under the proposed framework, as compared to other established yet simpler modeling techniques.

Ensemble streamflow prediction (ESP), which is generally achieved by combining ensemble weather forecast (EWF) and hydrological model, has a wide application. However, the EWF is biased and underdispersive, and therefore cannot be directly used to build ESP. The skillful forecast lead time of EWF in ESP needs to be determined, and the uncertainty of hydrological models is also nonnegligible. In this study, raw meteorological forecasts are corrected by the generator-based postprocessing method (GPP), the skillful forecast lead time of EWF is determined by comparison with a historical resampling method, and hydrological model uncertainty is investigated using Bayesian model average. The results indicate that GPP can significantly reduce bias and improve dispersion. With a superior postprocessing method, the skillful forecast lead times are 9 and 14 lead days for precipitation and temperature, respectively. With the synthetic effects of precipitation and temperature, the ESP has a skillful forecast lead time for around 10 lead days in terms of both deterministic and probabilistic metrics. However, the skillful lead time may be shortened to 5 days for flood season streamflow predictions. In addition, the hydrological model is an important source of uncertainty in ESPs, especially when evaluating ESPs in terms of probabilistic metrics. The ESP based on a combination of multiple hydrological models outperforms that based on a single model. Overall, this study indicates that the combination of postprocessed EWFs and multiple hydrological models is an effective approach for ESPs.

Urban flooding poses an increasing threat to citizens and property and has become a global challenge. The primary cause of urban flooding is rapid urbanization, which is outpacing infrastructure development and drainage system improvements. This paper utilizes a verified flood model that couples hydrological and hydrodynamic processes to investigate the flood response under rainfall events with different patterns. A sponge city pilot area in China is taken as a case study. Drainage system performance, surface inundation patterns, and relation between them are investigated by carrying out statistical analysis and Pearson correlation analysis, respectively. Results show that with increases in the rainfall recurrence interval (RRI) and peak position ratio (PPR), drainage system performance indicators (DSPIs) generally become worse, and the total inundation area (TIA) and surface inundation areas of larger depths become larger. The response time exhibits varied patterns but generally decreases with increasing recurrence intervals and peak ratios. For surface inundation, the recurrence interval presents an increasingly significant role compared with the peak ratio as rainfall becomes heavier. Compared with other DSPIs, the volume of overflowed manholes (VOMs) shows a delayed response to rainfall and has a stronger relationship with the inundation areas with greater water depths, which indicates a possible solution of utilizing manhole storage for water management and flood control. Analyzing the response of drainage systems and flood patterns to different rainfall events using a model coupling hydrological and hydrodynamic processes comprehensively reveals the specific problems of the study area and possible innovative solutions. The analytical method and knowledge gained can help support urban flood management in China and around the world.

The linkages of precipitation extremes with large-scale global climatic phenomenon such as global warming, the El Niño–Southern Oscillation (ENSO), and the land surface elevation were explored across India. Temporally, the annual average precipitation on rainy days (PRCPTOT) was found to exhibit decreasing trends for a larger part of India; however, most of the extreme precipitation indexes (EPIs) had increasing trends (except cumulative wet days) for a larger part of India. In addition, most EPIs exhibited a positive and statistically significant correlation of high magnitude with the land surface elevation for the elevation band above 1,500 m. Parts of northern, north-central, and northeastern India had a decrease in precipitation extremes with an increase in the global temperature, whereas an increase in EPIs per degree Celsius increase in the average global temperature was found for the eastern coastal regions and most parts of southern India. A spatial heterogeneity of teleconnections between precipitation extremes and the large-scale climatic indexes was found. The Southern Oscillation Index (SOI) and the Northern Oscillation Index (NOI) had similar spatial patterns of correlation with the analyzed EPIs. Most EPIs except consecutive dry days (CDD) and consecutive wet days (CWD) had higher positive association with extreme events in the contiguous region of Rajasthan, Madhya Pradesh, Uttar Pradesh, and West Bengal. The NINO3 and NINO3.4 sea surface temperature (SST) indexes had patches of higher correlation over south-central India. Moreover, teleconnections of ENSO with CDD were strongest with stronger correlation values and higher percentage of area with significant correlation compared with other indexes.

For decades many authors have used entropy-based models to predict velocity distribution in open channel flows. Such models use maximum and mean velocity in an assigned domain to obtain velocity distribution. However, while the signification of maximum velocity is clear, the use of mean velocity is sometimes misleading. This paper thus clarifies, for the case of wide channel flows, both, the signification and role of mean velocity within certain existing entropy-based models. It also demonstrates, for some widely cited approaches, the need to pay attention to the difference between the expected value of velocity and the average cross-sectional velocity.