Journal of Hydrologic Engineering

Current probabilistic methods for maximum and minimum run lengths in a hydrologic time series need further development. Using the first-order Markov chain model and an extreme-value theory for randomly occurring events, this study investigates probabilistic properties of both extreme run lengths in a stationary hydrologic time series. The interaction between negative run length, positive run length, total run length, life span, and number of total runs was analyzed. As a result, a joint probabilistic space was defined for negative (or positive) and total run lengths, and a corrector was established for an existing cumulative distribution function (CDF) of negative run length. Lengths of incomplete runs were considered appropriately, especially when no total run occurred during the life span. A generalized CDF was formulated for each extreme value, and simplified versions of the generalized CDF were proposed as practical approximate methods, including expectation approximations and limiting approximations. Results of computational examples indicated that expectation approximations generally provided better results than limiting approximations for maximum negative run lengths, and the non-negligible probability of no total run occurring during the life span may have a significant influence on CDFs.

Hydrological forecasting is key for water resources allocation and flood risk management. Although a number of advanced hydrological forecasting methods have been developed in the past, daily (or subdaily) forecasting remains a major challenge in engineering hydrology. The uncertainties associated with input data, model parameters, and model structure necessitate developing more robust modeling techniques. In this study, a hybrid machine-learning approach based on hydrological and data-driven modeling is developed for daily streamflow forecasting. The proposed hybrid hydrological data-driven model (HHDD) approach succeeds in improving daily prediction compared to that predicted by the standard conceptual hydrological model (HYMOD). In addition, the developed HHDD model is more robust in terms of providing direct uncertainty analysis results. The results indicate that a better resemblance of streamflow pattern is achieved by integrating physically based and data-driven approaches into the developed HHDD model.

This paper studies the effects of different types and configurations of double layer vegetation on the flow of open channels. The vegetation is simulated through cylindrical dowels with a diameter of 6.35 mm and heights of 10 and 20 cm, which represent short and tall dowels, respectively. Profiles for instantaneous velocities were obtained by acoustic Doppler velocimetry (ADV) at different locations around vegetation with multiple staggered and linear formations. The experiment covers a wide range of sparse to dense vegetation configurations. Furthermore, different flow depths were selected to simulate fully submerged cases for short vegetation and to capture the inflection of velocity over the mixing region between short and tall dowels. The results reveal that the velocity profile is mostly uniform at the depth of short vegetation in different configurations with various densities. The velocity starts to increase in the region near the top edge of short vegetation, followed by a significant increase through the height of tall vegetation to the free surface. Generally, the flow velocity behind the vegetation layer is significantly smaller than that in free regions adjacent to short and tall vegetation. The overall idea of the present study was to simulate the same sets of vegetation configurations using a $\mathrm{K}\text{-}\epsilon$ model with mesh sensitivity analysis to capture inflections over the short vegetation region. The experimental investigations with a numerical study were explored for double layer vegetation, which was corroborated and found to have good agreement for different vegetation configurations.

The deployment of real-time control (RTC) technology has improved the performance of stormwater management facilities. However, most of the current research focuses on individual pilot sites. The major challenge hindering the upscaling of RTC deployment is the lack of a theoretical framework that guides real-time operation of a network of interconnected reservoirs. This study suggests a hybrid approach for enhancing a systemwide operation. The proposed model coupled a real-time control algorithm using two dynamic reservoir-release components with static releases from the storm water management model (SWMM5). This model was applied to the Merrybrook Run watershed in Fairfax County, Virginia. The results showed enhanced system performance and better use of available latent storage to mitigate downstream impacts.

Digital elevation models (DEMs) are widely used for hydraulic modeling, but one of the challenges in using a DEM for hydraulic modeling is its limitations in accurately representing the channel conveyance. This limitation is usually disregarded because the collection of bathymetry data is time and resource intensive, and it is often unavailable for well-gauged watersheds as well. Thus, a DEM-based hydraulic model may lead to less accurate conclusions even if detailed hydrologic data are available. The situation is more challenging in large and morphologically active river systems because DEMs only represent the river condition of their acquisition time. Therefore, the objectives of this research are to (1) develop a DEM correction method for predicting channel conveyance at the watershed scale and (2) demonstrate the significance of riverbed-level change for flood risk assessment in the Obion River. The proposed DEM correction method is called Slope Adjusted Mean Bed Level Elevation (SAMBLE) and utilizes the hydrologic data of a watershed to predict average riverbed level and thus channel conveyance at a watershed scale. It is tested on the Obion River watershed in northwest Tennessee. Analysis of the results showed that the SAMBLE method can predict average riverbed level with less than 1 m error that is more accurate than the best available Light Detection and Ranging (LiDAR) DEM for the Obion River watershed. Additionally, it can predict riverbed-level changes using time series of hydrologic data. The significance of riverbed-level change on flood risk is also demonstrated by estimating aggradation-degradation (via SAMBLE) in a morphologically active segment of the Obion River.