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Wetlands protect downstream waters by filtering excess nitrogen (N) generated from agricultural and urban activities. Many small ephemeral wetlands, also known as geographically isolated wetlands (GIWs), are hotspots of N retention but have received fewer legal protections due to their apparent isolation from jurisdictional waters. Here, we hypothesize that the isolation of the GIWs make them more efficient N filters, especially when considering transient hydrologic dynamics. We use a reduced complexity model with 30 years of remotely sensed monthly wetland inundation levels in 3700 GIWs across eight wetlandscapes in the US to show how consideration of transient hydrologic dynamics can increase N retention estimates by up to 130%, with greater retention magnification for the smaller wetlands. This effect is more pronounced in semi-arid systems such as the prairies in North Dakota, where transient assumptions lead to 1.8 times more retention, compared to humid landscapes like the North Carolina Pocosins where transient assumptions only lead to 1.4 times more retention. Our results highlight how GIWs have an outsized role in retaining nutrients, and this service is enhanced due to their hydrologic disconnectivity which must be protected to maintain the integrity of downstream waters.

 

Groundwater recharge moves downward from the land surface and reaches the groundwater to replenish aquifers. Despite its importance, methods to directly measure recharge remain cost and time‐intensive. Recharge is usually estimated using indirect methods, such as the widely used water‐table fluctuation (WTF) method, which is based on the premise that rises in groundwater levels are due to recharge. In the WTF method, recharge is calculated as the difference between the observed groundwater hydrograph and the hydrograph obtained in the absence of water input. The hydrograph in the absence of rise‐producing input is estimated based on a characteristic master recession curve (MRC), which describes an average behavior for a declining water‐table. Previous studies derive MRC using recession data from all seasons. We hypothesize that for sites where groundwater table is shallow, using recession data from periods with high groundwater‐influenced evapotranspiration (ET) rates versus all periods will yield significantly different MRC, and consequently different estimates of recharge. We test this hypothesis and show that groundwater recession rates are significantly greater in warm months when the groundwater‐influenced ET rates are higher. Since obtaining seasonal recession rates is challenging for locations with a limited amount of data and is prohibitive if it is to be obtained for any given season of a particular year, we propose two novel parsimonious methods to obtain recession time constants for distinct seasons. The proposed methods show the potential to significantly improve the estimates of seasonal recession time constants and provide a better understanding of seasonal variations in recharge estimates.

 

Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications. 

Brominated disinfection byproducts (DBPs) are a concern to drinking water utilities due to their toxicity and increasing prevalence in water systems. Haloacetic acids (HAAs) are a class of DBPs that are partially regulated by the United States Environmental Protection Agency (USEPA), but regulations are likely to increase as evidenced by the brominated HAAs listed on the USEPA Fourth Unregulated Contaminant Monitoring Rule and Fifth Contaminant Candidate List. Utilities often use a pre-oxidant to assist in their treatment training, but this can lead to increased HAA formation during treatment. In this study, tap water was spiked with bromine (Br2) at varying concentrations to simulate bromine-to-chlorine ratios found in the natural environment and the DBPs that may be formed from those waters. The water was fed through a bench-scale biological filter (biofilter) with a small layer of fresh granular activated carbon (GAC) media followed by acclimated anthracite media. The HAA species studied were found to be removable by an average of 89.5% through combined GAC filtration and biofiltration. Biodegradation occurred predominantly in the first five minutes for the acclimated anthracite, with minimal additional removal observed at longer empty bed contact times (15 and 30 min EBCT). This study provides recommendations on biofilter parameters for utilities to reduce the formation of both regulated and unregulated HAAs during the drinking water treatment process.