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Understanding suspended sediment dynamics and their driving factors is essential for effective watershed management. In this study, the spatiotemporal patterns of suspended sediment were identified for 11 sub‐watersheds of the Goodwin Creek, Mississippi, USA, for the period 1982–2002 using wavelet transforms. The continuous wavelet transform (CWT) results of streamflow time series showed that a near‐continuous annual periodicity influenced all stations over the study period, yet an overall decline occurred in wavelet spectral power of sediment load during the 1990s. This decline in sediment load is a result of the indirect effect of decreased cultivation on reducing the rate of peak flow and thus fluvial erosion processes. The CWTs of fine sediment (clays and silts) concentration (FSC) time series showed that factors other than land‐use (e.g., ponds, ephemeral gullies, and bed composition) better explain the variations in FSC. A comparison of CWTs of FSC at two stations with different bed composition in their channels showed that sandy channels are less responsive to erosion control measures compared to gravely channels indicating that they are major sources of sediment. A change in phase relationship between flow and FSC for the gravel‐dominated channel was associated with the transition in upland land‐use indicating a change in sediment supply mechanisms. The CWTs of sand concentrations reflected the spatiotemporal impact of bank materials, the growth cycle of riparian vegetation, and in‐channel sand availability on sand dynamics. While CWT‐based analysis is sensitive to input conditions, the method is able to provide valuable outcomes for distinguishing patterns in complex temporal datasets. Key Points: An overall decline in fine sediment load occurred across all time scales during the period of land‐use change and in‐channel stabilizationStreambed composition and the locations of ponds and upland sediment sources explain the spatiotemporal patterns of suspended finesThe spatiotemporal patterns of sand dynamics reflect both the state of channel stability and the availability of in‐channel sand [ABSTRACT FROM AUTHOR]

An empirical framework for assisting with water quality management is proposed that relies on open-source hydrologic data. Such data are measured periodically at fixed water stations and commonly available in time-series form. To fully exploit the data, we suggest that observations from multiple stations should be combined into a single long-panel data set, and an econometric model developed to estimate upstream management effects on downstream water quality. Selection of the model's functional form and explanatory variables would be informed by rating curves, and idiosyncrasies across and within stations handled in an error term by testing contemporary correlation, serial correlation, and heteroskedasticity. Our proposed approach is illustrated with an application to the Nakdong River basin in South Korea. Three alternative policies to achieve downstream BOD level targets are evaluated: upstream water treatment, greater dam discharge, and development of a new water source. Upstream water treatment directly cuts off incoming pollutants, thereby presenting the smallest variation in its downstream effects on BOD levels. Treatment is advantageous when reliability of water quality is a primary concern. Dam discharge is a flexible tool, and may be used strategically during a low-flow season. We consider development of a new water corridor from an extant dam as our third policy option. This turns out to be the most cost-effective way for securing lower BOD levels in the downstream target city. Even though we consider a relatively simple watershed to illustrate the usefulness of our approach, it can be adapted easily to analyze more complex upstream-downstream issues. [ABSTRACT FROM AUTHOR]

Fluvial sediment loads are frequently calculated with rating curves fit to measured sediment transport rates. Rating curves are often treated as statistical representations in which the fitted parameters have little or no physical meaning. Such models, however, may produce large errors when extrapolation is needed, and they provide no insight into the sediment transport process. It is shown that log-linear least squares, the usual method for fitting rating curves, does not generally produce physically meaningful parameter values. In addition, it cannot accommodate data that include zero-transport samples. Alternative fitting methods based nonlinear least squares and on maximum likelihood parameter estimation are described and evaluated. The maximum likelihood approach is shown to fit synthetic data better than linear or nonlinear least squares, and to perform well with data that include zero-transport samples. In contrast, nonlinear least squares methods produce large errors in the parameter estimates when zero-transport samples are present or when the variance structure of the data is incorrectly specified. Analyses with fractional bedload data from a mountain stream suggest that bedload transport rates are gamma distributed, that the arrivals of bedload particles in a sampler conform to a Poisson distribution, and that the variance of nonzero samples can be expressed as a power function of the mean. Preliminary physical interpretations of variations in the rating curve parameters fit to fractional bedload data with the maximum likelihood method are proposed, and their relation to some previous interpretations of rating curve parameters are briefly discussed. [ABSTRACT FROM AUTHOR]

Recent floods from intense storms in the southern United States and the unusually active 2017 Atlantic hurricane season have highlighted the need for real‐time flood inundation mapping using high‐resolution topography. High‐resolution topographic data derived from lidar technology reveal unprecedented topographic details and are increasingly available, providing extremely valuable information for improving inundation mapping accuracy. The enrichment of terrain details from these data sets, however, also brings challenges to the application of many classic approaches designed for lower‐resolution data. Advanced methods need to be developed to better use lidar‐derived terrain data for inundation mapping. We present a new workflow, GeoFlood, for flood inundation mapping using high‐resolution terrain inputs that is simple and computationally efficient, thus serving the needs of emergency responders to rapidly identify possibly flooded locations. First, GeoNet, a method for automatic channel network extraction from high‐resolution topographic data, is modified to produce a low‐density, high‐fidelity river network. Then, a Height Above Nearest Drainage (HAND) raster is computed to quantify the elevation difference between each land surface cell and the stream bed cell to which it drains, using the network extracted from high‐resolution terrain data. This HAND raster is then used to compute reach‐average channel hydraulic parameters and synthetic stage‐discharge rating curves. Inundation maps are generated from the HAND raster by obtaining a water depth for a given flood discharge from the synthetic rating curve. We evaluate our approach by applying it in the Onion Creek Watershed in Central Texas, comparing the inundation extent results to Federal Emergency Management Agency 100‐yr floodplains obtained with detailed local hydraulic studies. We show that the inundation extent produced by GeoFlood overlaps with 60%~90% of the Federal Emergency Management Agency floodplain coverage demonstrating that it is able to capture the general inundation patterns and shows significant potential for informing real‐time flood disaster preparedness and response. Plain Language Summary: Simple and computationally efficient flood inundation mapping methods are needed to take advantage of increasingly available high‐resolution topography data. In this work, we present a new approach, called GeoFlood, for flood inundation mapping using high‐resolution topographic data. This approach combines GeoNet, an advanced method for high‐resolution terrain data analysis, and the Height Above Nearest Drainage. GeoFlood can rapidly convert real‐time forecasted river flow conditions to corresponding flood maps. A case study in central Texas demonstrated that the flood maps generated with our approach capture the majority of the inundated extent reported by detailed Federal Emergency Management Agency flood studies. Our results show that GeoFlood is a valuable solution for rapid inundation mapping. Key Points: A simple and computationally efficient approach was developed for real‐time flood inundation mappingThe approach, called GeoFlood, combines nonlinear filtering, geodesic minimization principles, prior information from low‐resolution products, and the Height Above Nearest DrainageWith simple hydraulic assumptions and approximate roughness estimation, GeoFlood rapidly generates flood maps corresponding well with those of detailed local models [ABSTRACT FROM AUTHOR]