High-resolution topographic data reveal that meandering river floodplains can contain complex floodplain-channel networks. However, the influence of this topography on flood initiation and progression is unknown. In this study, we investigate how floodplain-channel networks influence flooding processes in low-gradient river systems. To accomplish this, we conducted a series of numerical modeling experiments using a two-dimensional (2-D) model built in Hydrologic Engineering Center’s River Analysis System (HEC-RAS). First, we simulated floods using 2-D HEC-RAS on the East Fork White River near Seymour, IN, USA, including the current, dense network of floodplain channels. Next, we simulated synthetic versions of this river system where we varied the connectivity among floodplain channels, and also between the floodplain channels and river channel. We found distinct differences in flooding patterns and flood hydraulics among model simulations. Numerical modeling experiments showed that increasing floodplain channel connectivity caused increased spatial variability of wet and dry surfaces across the floodplain, increased residence time of water on the floodplain, and increased floodwave attenuation. Results from this study indicate that topographic connectivity on the floodplain challenges the classic notions of flooding initiation, and potentially increases a floodplains ability to store matter.

Recent revisions to the definition of the “waters of the United States” (WOTUS) have considerably altered how wetlands are federally regulated under the Clean Water Act. The two most recent modifications to WOTUS, the Clean Water Rule (CWR) and the Navigable Waters Protection Rule (NWPR), represent two opposing approaches to the federal wetland policy. Despite their implementation, the impacts of these rules on the regulation of wetlands have as of yet been poorly characterized at broad spatial scales. Using New York State (NYS) as a case study, we evaluated the jurisdictional statuses of more than 373,000 wetlands under the CWR and the NWPR to assess the landscape-scale effects of WOTUS re-definitions. We found that statewide and within each of NYS’s hydrologic regions, the NWPR protects fewer wetlands and less total wetland area than the CWR. The efficacy of the two regulations varied considerably in space across NYS, highlighting the need for comprehensive, nationwide assessments of wetland policy outcomes. We also observed that both rules produced non-uniform patterns in jurisdiction across a range of landscape positions and wetland sizes, preferentially protecting large wetlands close to the stream network. This effect was particularly pronounced under the NWPR, which excludes all geographically isolated wetlands from protection. Our findings in NYS emphasize the existence of unique patterns in protected wetlands across spatial scales, highlighting the value in applying geospatial analyses to evaluate environmental policy.

Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co-locating water quality observations with established stream gauges. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin - one of the U.S.’s most nutrient polluted basins - using the established Agro-IBIS model. While real-world observations are limited in space and time, particularly for nitrate, the synthetic data set allows for sufficiently long periods to train machine learning models and assess their performance. Using the synthetic data, we established baseline 1-day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48-3.3 mg/L). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional sensors. Synthetic data enable us to quantitatively assess the expected value of an additional nitrate sensor being deployed, which is, of course, not possible if we are limited to the present observational network. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at all possible locations. Finally, we assessed the co-benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for AI to make short-term predictions and provide an unbiased assessment of the marginal benefit and co-benefits to an expanded sensor network. While we use water quantity in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.