Journal of Hydrologic Engineering

Water resources are crucial for human activities and sustainable socioeconomic development. Understanding surface water information can play a key role in water resource management, which affects the global water cycle and ecological environments. Considering the Hailar River water body as an example, this study proposes a new threshold self-learning water body extraction method (TSLWEM) based on modified normalized difference water index (MNDWI) data. The optimal water extraction thresholds determined by the TSLWEM algorithm for four test images were $-0.0030$, 0, 0.1990, and $-0.0800$. The TSLWEM algorithm effectively identified the target water body with recognition accuracies of 98.08%, 99.93%, 93.39%, and 93.20% for the four test images. Moreover, it can accurately identify small tributaries, such as lakes and rivers. The TSLWEM algorithm is suitable for Landsat 8 Operational Land Imager (OLI) data, which can effectively monitor and map complex surface water in temperate and semiarid regions while improving the accuracy of water body identification. The study’s findings provide technical support for the protection of water resources as well as their rational utilization and monitoring.

Abstract

To adequately define risk in an operational setting, modeling and data uncertainty must be addressed. Though metrics to evaluate model performance are numerous in the literature, few integrate either modeling uncertainty or benchmark data uncertainty, and even fewer integrate both. The Combined Overlap Percentage (COP) ensemble metric is a notable exception: it is based on optimizing the trade-off objectives of maximizing the overlap between simulated and benchmark uncertainty bounds (overlap-reliability) while minimizing simulated ensemble uncertainty bound width (overlap-sharpness) with equal weight. We further develop the COP by assessing weighting methods to increase applicability to additional types of benchmark data uncertainty. As new advanced datasets are generated each year, the weighted COP can integrate ensembles of benchmark data rather than forcing modelers to attempt to identify the best product at a low computational cost. The new weighting method further allows the COP to adapt to the unique features of those new datasets. Results suggest increasing the weight of overlap-sharpness when robust benchmark uncertainty estimates are available. Conversely, higher weights should be given to overlap-reliability when little benchmark uncertainty information is available. Finally, timestep weighting and data transforms are only impactful if overlap-sharpness is prioritized. The results are particularly relevant in an operational context and could allow for the integration of uncertainty into calibration and ensemble generation at a low computational cost.

Practical Applications

Uncertainty represents the expected range a perfect measurement would span if it were possible to collect a perfect measurement. In modeling applications, this range expands to include the imperfections of the model. Recognizing this, uncertainty in modeling applications has generally led to the creation of over-confident models fit to, or evaluated against, imperfect data that are assumed to be perfect. Including uncertainty bounds representing a range of observations when evaluating a model is not often considered despite many studies highlighting its importance; this is referred to as output uncertainty. Here the authors further develop the Combined Overlap Percentage (COP) with new weighting factors, which allows for the integration of observation, or benchmark, data uncertainty in an accessible and computationally reasonable way. New weighting schemes allow the updated COP to capture periods of low data uncertainty, which allows for higher quality data (that for which there is higher confidence in) to have a greater influence on the model. The authors test the weighted COP with three case studies to explore how much information is needed to apply this method, and how weighting schemes perform with different amounts of uncertainty information. Finally, the authors make recommendations based on the results generated by the case studies.

Deicing salts are a major contributor to freshwater salinization in the Northeastern United States, disrupting ecosystem function, aquatic life, and infrastructure integrity. It is often presumed that surface runoff is the dominant mode of chloride transport from impervious surfaces to surface waters despite empirical evidence for soil and groundwater transport. Our project aimed to (1) estimate the hydrologic pathways for chloride transport in a small, urbanized catchment in the Northeastern United States; and (2) provide a critical evaluation of simulation routines commonly used to guide the management of deicing salts. We investigated possible variations in urban chloride pathways by monitoring stormwater discharge and chloride across a winter season with deicing salt spreading and snow accumulation and melt. We analyzed these time series with US EPA SWMM, which presumes chloride transport occurs exclusively via surface runoff into stormwater systems. We develop two parallel models: one using the base empirical temperature-index snow routines, and a second substituting a process-based snowmelt model. Each model was calibrated via the generalized likelihood uncertainty estimation algorithm to both stormwater volumes and chloride mass. Both models adequately simulated water volumes but failed to reproduce observed chloride dynamics, suggesting substantial deicing salt movement through soils and groundwater as a major urban hydrologic pathway. The results of this study will inform the development of urban hydrologic fate and transport models and chloride best management practices.

The present paper studies the two-dimensional unsteady suspended sediment transport problem through an open channel turbulent flow that carries large amounts of sediments. Presence of large amounts of sediments causes separate, distinct horizontal layers that result to differences in density, which is called density stratification. Also, due to interactions with neighboring particles, a particle settles at a reduced speed relative to that of a single particle in clear fluid, which is called hindered settling. The model includes both of these effects and the governing equation, which is a nonlinear partial differential equation (PDE), has been solved by the finite difference (FD) method. The model has been validated with existing models under certain specific conditions. It is found that stratification causes a decrement in sediment concentration values and the effect becomes prominent as time increases until it reaches a stable value. A reverse result is obtained for the case of hindered settling and both the effects are visible mainly in the main suspension region. Finally, the model has been validated with a few existing models together with some experimental data at steady-state and far-field conditions.