In recent years, efforts to assess the evolving risks of coastal compound surge and rainfall-driven flooding from tropical cyclones (TCs) and extratropical cyclones (ETCs) in a warming climate have intensified. While substantial progress has been made, the persistent challenge lies in obtaining actionable insights into the changing magnitude and spatially-varying flood risks in coastal areas. We employ a physics-based numerical hydrodynamic framework to simulate compound flooding from TCs and ETCs in both current and future warming climate conditions, focusing on the western side of Buzzard Bay in Massachusetts. Our approach leverages hydrodynamic models driven by extensive sets of synthetic TCs downscaled from CMIP6 climate models and dynamically downscaled ETC events using the WRF model forced by CMIP5 simulations. Through this methodology, we quantify the extent to which climate change can potentially reshape the risk landscape of compound flooding in the study area. Our findings reveal a significant increase in TC-induced compound flooding risk due to evolving climatology and sea level rise (SLR). Additionally, there is a heightened magnitude of compound flooding from ETCs, in coastal regions, due to SLR. Inland areas exhibit a decline in rainfall-driven flooding from high-frequency ETC events toward the end of the century compared to the current climate. Our methodology is transferable to other vulnerable coastal regions, serving as a valuable decision-making tool for adaptive measures in densely populated areas. It equips decision-makers and stakeholders with the means to effectively mitigate the destructive impacts of compound flooding arising from both current and future TCs and ETCs.

A document by Danica Roth. Click on the document to view its contents.

Many water markets in the Western United States (U.S.) have the ability to reallocate water temporarily during drought, often as short-term water rights leases from lower value irrigated activities to higher value urban uses. Regulatory approval of water transfers, however, typically takes time and involves high transaction costs that arise from technical and legal analyses, discouraging short-term leasing. This leads municipalities to protect against drought-related shortfalls by purchasing large volumes of infrequently used permanent water rights. High transaction costs also result in municipal water rights rarely being leased back to irrigators in wet or normal years, reducing agricultural productivity. This research explores the development of a multi-year two-way option (TWO) contract that facilitates leasing from agricultural-to-urban users during drought and leasing from urban-to agricultural users during wet periods. The modeling framework developed to assess performance of the TWO contracts includes consideration of the hydrologic, engineered, and institutional systems governing the South Platte River Basin in Colorado where there is growing competition for water between municipalities (e.g., the city of Boulder) and irrigators. The modeling framework is built around StateMod, a network-based water allocation model used by state regulators to evaluate water rights allocations and potential rights transfers. Results suggest that the TWO contracts could allow municipalities to maintain supply reliability with significantly reduced rights holdings at lower cost, while increasing agricultural productivity in wet and normal years. Additionally, the TWO contracts provide irrigators with additional revenues via net payments of option fees from municipalities.

Coastal zones are particularly vulnerable to flooding. Several climatic and state variables may drive the occurrence of such events, e.g., storm surges, sea level rise, heavy rainfall, and high river and groundwater levels. The co-occurrence of such events, i.e. compound or cascading effects, has been shown to escalate flooding impacts and extent, but the contribution of groundwater is routinely overlooked. Here, we apply an integrated hydrological/hydrodynamic/groundwater model to investigate underlying causes and compound effects in a Danish Wadden sea catchment. Two models were developed: a long-term model and an overbank-spilling model. The long-term model was calibrated and used to simulate 30-year periods. Extreme value analyses were carried out for sea levels, precipitation, simulated river water stages, and groundwater levels. The co-occurrence of extremes was used to identify compound effects on high river-stage incidents (as a flood proxy). The overbank-spilling model was then used for simulating flooding for a subset of the largest river stage events identified from the long-term model. The analysis showed that the river-stage events were closely correlated to the sea level extremes, but that the largest river-stage events were almost exclusively compounded by precipitation or groundwater, or both. High groundwater tables seem to correlate to the flooding events with the largest spatial extent, as well as prolonged extreme events where either precipitation or sea level were elevated during long periods. Thus, this study shows that there is a general need to acknowledge the potential effect of groundwater levels on the resulting flooding on terrain in coastal zones.

This study assessed total hydrocarbon content and polycyclic aromatic hydrocarbon content in Grönfjord (the Greenland Sea, Svalbard). The field study was held in marine expeditions of research vessel “Barentsburg” by the North-Western Branch of the Federal State Budget Institution, Research and Production Associaton «Typhoon» in summer periods of 2012 to 2022. In the framework of the field works simultaneous measurements of hydrological and hydrochemical characteristics of the water column were done. The data was analyzed using standard procedure in purpose to gather new information about the levels of hydrocarbons    (measured as total hydrocarbon contents), polycyclic aromatic hydrocarbons. The results showed pronounced interannual variations of total hydrocarbon contents and polycyclic aromatic hydrocarbons concentrations. Supposed that local natural sources contribute to elevated polycyclic aromatic hydrocarbons and total hydrocarbon content levels both in water and in sediments,  the levels of contamination do not signify exclusively anthropogenic influence on the sea-body. At the same time, some local elevated petroleum hydrocarbons concentrations, which were detected in the surface water layer, may be a sign of existing industrial activity affecting the waters of the fjord. Continuity of tasks starting from earlier expeditions indicates that many processes in the Norwegian Sea, Greenland Sea require further research.

A document by Zachary Johnson. Click on the document to view its contents.

Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. When part of numerical weather prediction, ULSMs provide a lower boundary for the atmosphere and improve the applicability of model results in the urban environment compared with non-urban land surface models. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban-PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators. We find that the water budget is only closed in 57% of the model-site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all good water balance indicators for any site. Our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

The isotopic effects of sublimating ice is poorly understood and disagreement from diverging results from studies spans decades. The core question is whether sublimation occurs layer-by-layer with no fractionation or whether diffusion within the ice and vapor-ice exchange generate fractionation. Here, small ice spheres were suspended in an unsaturated atmosphere and a Rayleigh distillation model was used to estimate fractionation of the spheres. A small, yet statistically significant and repeatable, fractionation (103lna18O of ~ -0.6‰ (ɑ = 0.999) and 103lna2H -3 to -6‰ (ɑ = 0.994 to 0.997 ) was observed, smaller than predicted for equilibrium fractionation at this temperature and humidity. Assuming a modest porosity of 0.0005%, porosity could sufficiently increase diffusivity to explain the observed fractionation. The results help reconcile how sublimation varies between experimental and observational studies where uncontrolled porosity varies substantially across a continuum from porous firn layers to low porosity ice deep in glaciers.

In the Western U.S., the combination of increased and projected droughts, rising irrigation water demands, and population growth is expected to intensify groundwater consumption leading to adverse consequences like land subsidence and aquifer depletion. Despite the urgent need to address these challenges, there is limited local-scale monitoring of groundwater withdrawals in most of the groundwater basins in this region. Understanding the volume of groundwater being withdrawn is indispensable for implementing sustainable solutions to tackle water security issues. Hence, developing reliable and efficient groundwater withdrawal monitoring solutions is critical to address the pressing water management concerns in this region. The existing methods for estimating groundwater withdrawals are either costly and time-consuming, or they cannot generate dependable predictions at the scales required for effective local management. While our earlier works on integrating remote sensing and machine learning techniques to estimate gridded (1-5 km) groundwater use in Kansas, Arizona, and the Mississippi Alluvial Plain have been successful, field-scale estimation is still a challenge. Here, we use statistical and machine learning-based approaches to relate field-scale groundwater withdrawals with remote sensing-derived datasets, e.g., Landsat evapotranspiration (ET), downscaled SMAP surface soil moisture, and other hydrometeorological datasets for several Western U.S. states. We apply and test our approach by estimating and comparing groundwater pumping measurements at field- and regional-scales for multiple groundwater basins. Preliminary results using linear regression and machine learning-based approaches in Nevada and Arizona show promise (R2 of 0.5 to 0.7), additional in-situ pumping data actively being compiled will likely improve the models. While there are clear opportunities for model improvements, modeled withdrawal estimates are likely more accurate than common water right duties and potential crop ET-based estimates. We aim to enable water resource and user communities better understand water use, water budgets and support field-scale management practices for metered and unmetered groundwater basins.

Space gravity missions have enabled the quantification of ocean mass increase over the past two decades due to exchanges between continents and oceans. Globally, non-steric sea level rise is predominantly driven by melting polar ice sheets and mountain glaciers. However, continental hydrological processes also contribute to sea level change at significant magnitudes. We show that for most coastal areas in low-to-mid latitudes, up to half of local non-steric sea level rise is due to changes in water storage in ice-free continental regions. At other locations the direct attraction effect of anthropogenic pumping of groundwater over the duration of the GRACE and GRACE-FO mission offsets sea level rise from ice sheet and glacier melt. If these trends in continental hydrological storage were to slow or stop, these regions would experience greatly accelerated sea-level rise, posing a risk to coastal settlements and infrastructure, however, sea level rise elsewhere would be reduced.

Streams are important freshwater habitats in large-scale CO2 emissions budgets because they are generally supersaturated with dissolved CO2. High CO2 concentrations driven by terrestrial carbon inputs, groundwater flow, and internal respiration vary greatly across space and time. We compiled and used environmental monitoring data to calculate CO2 concentrations along with a wide range of predictor variables and trained machine learning models to predict spatially distributed seasonal CO2 concentrations in Danish streams. We included outputs from a national hydrological model to investigate the influence of hydrological processes. We found that CO2 concentrations in streams were supersaturated (mean = 118 µM) and higher during autumn and winter than during spring and summer. The best model, a Random Forest model, which scored R2 = 0.46, MAE = 46.0 µM, and ⍴ = 0.72 on a test set, predicted seasonal CO2 concentrations for the entire stream network. The most important predictor variables were catchment slope, seasonality, elevation relative to the nearest stream, and depth to groundwater, which highlights the importance of landscape morphometry and soil-groundwater-stream connectivity. Stream CO2 fluxes, calculated by using empirical relationships, averaged 253 mmol m-2 d-1, and the annual emissions were 513 Gg CO2 from the national stream network (area = 139 km2). Our analysis presents a framework for modeling seasonal CO2 concentrations and estimating fluxes at a national scale by means of large-scale hydrological model outputs. Future efforts should consider further improving the temporal resolution, direct measurements of fluxes and gas transfer velocities, and seasonal variation in stream surface area.

The declining water level in Great Salt Lake (GSL) has been attributed to human consumptive water use that depletes natural streamflow into the lake. Understanding depletions due to historical consumptive water use within the GSL Basin is important to managing present and future lake conditions. Direct calculations of consumptive water use in the basin are made by summing detailed uses and return flows. However, this method is limited by insufficient data and resulting estimates thus far have been disparate. In this study, we reconstructed total GSL water inputs and stream inflows using lake levels recorded from 1847-2023 to estimate the magnitude of reductions due to consumptive use and the associated lake level decline. To do so, we developed a method that uses lake volume changes derived from bathymetry and water surface elevation measurements along with estimates of annual evaporation and precipitation over the lake to hindcast inflow volume to the lake. The declining trend in lake inflow, without associated precipitation or natural streamflow trends, was used to quantify basin wide water depletions to be up to 2.3 km3/yr and the current lake level decline associated with this estimate to be as much as 4.6 meters. This basin wide depletion estimate depends only on lake level, precipitation, and evaporation estimates and is not limited by the challenges of aggregating individual diversions and return flows.

Estuaries are dynamic coastal features that support industry, food production, and recreation, and provide habitat for numerous animal species. Their typically low surface gradients make estuaries vulnerable to sea level rise, storms, and high river water discharge. This vulnerability combined with the large number of people who often live near estuaries has led to increasing efforts over recent decades to improve our understanding of how to minimize flooding and protect people and property. Despite these efforts, however, we still lack the tools to quantify the relationship between changes in estuarine morphology and flood risks. In particular, the interplay between bathymetric changes and water levels during storm conditions remains poorly quantified. To address this knowledge gap, we present a general enthalpy framework for modeling the evolution of estuaries that couples a low gradient subaerial topset and a subaqueous offshore region or foreset. Sediment transport in both the subaerial and subaqueous domains includes a non-linear term that relates sediment flux, local slope, and a threshold of motion. With this approach, we describe the evolution of the bathymetric profile and sediment partitioning between topset and foreset under a range of sea-level variations scenarios. We find that in some cases upstream sections of the topset can undergo erosion during periods of sea-level rise and deposition during sea-level fall, contradicting traditional stratigraphic models. These counterintuitive bathymetric changes could potentially lead to shifts in the location of maximum water levels along the estuary not accounted for by models of storm inundation.

A document by PRARABDH TIWARI. Click on the document to view its contents.

Inundation of coastal stormwater networks by tides is widespread due to sea-level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies is unknown, but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (1) documented temporal variation in concentrations of Enterococcus spp. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a two-month period and more intensively during two perigean tide periods, (2) measured ENT concentrations in roadway floodwaters during tidal floods, and (3) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving water body vary as a function of tidal stage and antecedent rainfall, as well as site-specific characteristics of the stormwater network that drains to the waterbody. Tidal variables significantly explain measured ENT variance in the receiving waterway; however, runoff drove higher ENT concentrations. Samples of floodwaters on roadways during both perigean tide events were limited, but all samples exceed thresholds for safe public use of recreational water. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and in water pooling on roadways. These health hazards will likely be exacerbated in the future due to continued SLR.

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2) photon-counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat-2 measurements. Careful planning and dedicated development were accomplished during the pre-launch phase of the mission in preparation for the 2018 launch. Once on-orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat-2’s contributions to earth observation science initiated by NASA and the ICESat-2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat-2’s data capabilities and allowed for advanced science applications beyond the original mission objectives.

The Yangtze River Basin (YRB), home to around 400 million people, boasts of abundant water resources and significant spatial heterogeneity. Revealing the driving factors of water storage changes in YRB is essential for effective water resource management and sustainable development. In this study, we assess the drivers of total water storage (TWS) changes derived from the Gravity Recovery and Climate Experiment (GRACE) satellite within YRB from two perspectives: water balance and water storage components, including snow water equivalent (SWE), surface water storage (SWS), soil moisture storage (SMS), and groundwater storage (GWS). We also investigate the influence of reservoirs (e.g., Three Gorges Reservoir (TGR)), lakes (e.g., Dongting, Poyang, and Taihu), and glacier thawing on regional TWS changes. The results reveal an apparent increasing trend in YRB’s TWS from 2002 to 2022, while trends in precipitation, evapotranspiration, and runoff do not adequately account for this observed trend. In addition, our findings show that the increased TWS primarily occurs during the non-monsoon season, characterized by limited precipitation. The analysis of water components shows that the rise in TWS within YRB is predominantly attributed to GWS accumulation. SWS also contributes to the increasing TWS, primarily driven by the reservoir filling. The filling of TGR explains the observed TWS increase in Hubei province, whereas Lake Poyang accounts for about 30% of the positive TWS trend in Jiangxi province. Our comprehensive analysis systematically unveils the drivers of water storage changes in YRB, providing valuable insights for its sustainable water resource management and utilization.

Current machine learning methods for discharge prediction often employ aggregated basin-wide hydrometeorological data (lumped modeling) for parametric and non-parametric training. This approach may overlook the spatial heterogeneity of river systems and their impact on discharge patterns. We hypothesize that integrating temporal-spatial hydrologic knowledge into the data modeling process (distributed/disaggregated modeling) can improve the performance of discharge prediction models. To test this hypothesis, we designed experiments comparing the performance of identical Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models forced with either lumped or distributed features. We gather meteorological forcing and static attributes for the Mackenzie basin in Canada- a large and unique basin. Importantly, discharge performance is assessed out-of-sample with k-fold replication across gauges. Results reveal a 9.6% improvement in the mean Nash-Sutcliffe Efficiency (NSE) and a 4.6% improvement in mean Kling-Gupta Efficiency (KGE) when LSTMs are trained with distributed information. Notably, the models exhibit consistently unbiased predictions, with a negligible relative bias (RBias ≈ 0.0) across all predictions. These experiments and results demonstrate the importance of integrating topologically guided geomorphologic and hydrologic information (distributed modeling) in data-driven discharge predictions.

Hydrological observation networks are inadequate and declining, hindering advancements in hydrology, particularly in hydrological budgeting, where interception loss remains unmeasured, and a subjective loss figure is assumed. Measuring net rainfall reaching the ground surface is crucial for precise water balance estimates. This involves measuring throughfall & stemflow fractions of precipitation on the canopy. Current methods use collector channels and static storage systems requiring periodic visits for measurements, which becomes extremely difficult and unsafe in inaccessible forests, leading to data losses. For time-resolved data, an array of standard rain gauges is placed under the canopy, or water is collected by troughs for throughfall (and collars for stemflow measurement), draining into a central pipe and directed into a tipping bucket flow gauge. Commercial flow gauges are expensive for use in throughfall & stemflow measurement, since, multiple instruments are required in each study location to capture the variability of the canopy. To address this, we designed and fabricated an open-source, low-cost Tipping Bucket Flow Gauge to automatically monitor flow rates of throughfall & stemflow. It is designed to have a larger adjustable tipping resolution (10 ml – 200 ml). The open-source Arduino-based data logger automatically collects time-resolved data and is powered using solar energy, ensuring remote functionality even in harsh environments. A modular electronics approach was followed for designing the datalogger, facilitating rapid prototyping, easy repair, and upgrades, enabling someone with even an introductory knowledge in Arduino to implement the design. Almost 75% of the instrument is 3D printed and can be fabricated using any standard desktop FDM 3D printer and assembled by hand. The instrument showed 86% accuracy in preliminary testing at a calibrated tipping resolution of 120ml. The cost of prototyping was 100$ - 120$, thus proving cost-effective for accurate hydrological budgeting as compared to the cost of the nearest available commercial solution (~1800$). Through our research and product design, we intend to reduce the barrier of entry and simplify the steep learning curve faced in developing hydrological instrumentation.

Reliable air temperature forecasts are necessary for mitigating the effects of droughts and Heatwaves. The numerical weather prediction(NWP) model forecasts have significant biases associated and therefore need post-processing. Post-processing of temperature forecasts using probabilistic approaches are lacking in India. In this study, we post-process the Global Ensemble Forecast System (GEFS) and EuropeanCentre for Medium Range Weather Forecasts (ECMWF) NWP model temperature forecasts for short to medium range time scales (1-7 days)using two probabilistic techniques, namely, Bayesian model averaging(BMA) and Nonhomogeneous gaussian regression (NGR). The post-processing techniques are evaluated for temperature (maximum and minimum) predictions across the Indian region. Results show that the probabilistic approaches considerably enhance the temperature predictions across India except the Himalayan regions. These techniques also comprehensively outperform the traditional post-processing techniques such as the running mean and simple linear regression. The NGR performs better than the BMA across all regions and is able to provide highly skillful temperature forecasts at higher lead times as well. Further, the study also assesses the implication of probabilistic post-processing Tmax forecast towards forecast enhancement of heatwaves (HW) in India. Post-processed Tmax forecasts revealed that the NGR approach considerably enhanced the HW prediction skill in India, especially in the northwestern and central Indian regions, considered highly prone to HW. The findings of this study will be useful in developing enhanced HW early warning and prediction systems in India.

A document by Gonçalo Duarte. Click on the document to view its contents.

The present study focuses on heat transfer in ventilated caves for which the airflow is driven by the temperature contrast between the cave and the external atmosphere. We use a numerical model that couples the convective heat transfer due to the airflow in a single karst conduit with the conductive heat transfer in the rock mass. Assuming dry air and a simplified geometry, we investigate the propagation of thermal perturbations inside the karst massif. We perform a parametric study to identify general trends regarding the effect of the air flowrate and conduit size on the amplitude and spatial extent of thermal perturbations. Numerical results support the partition of a cave into three regions: (1) a short (few meters) diffusive region, where heat mainly propagates from the external atmosphere by conduction in the rock mass; (2) a convective region where heat is mainly transported by the air flow; (3) a deep karst region characterized by quasi-constant temperatures throughout the year. An estimation of the length of the convective region is proposed and compared to field data from a mine tunnel and two caves. Our results provide first estimates to identify climate sensitive regions for speleothem science and/or ecosystemic studies.

ABSTRACT Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to measure the extent of management-driven bias in fecal coliform observations collected in a particular area. 1. INTRODUCTIONHealthy estuarine environments are critical for maintaining ecological stability, coastal economies, and human health standards. In order to maintain and even improve these habitats, metrics of current and past conditions must be evaluated to inform proper management. Water quality measurements can be used to indicate overall estuarine health and can aid in understanding increasing coastal threats such as rising sea levels, increased salinities, and urbanization. Long-term water quality analysis is key for developing target thresholds for future management action as well as assessing the efficacy of past management measures (Cloern et al., 2016). The value of historical observations in advancing understanding of estuarine water quality has been demonstrated by multi-decadal studies of several systems, including the San Francisco Bay area (Beck et al., 2018; Cloern et al., 2016), May River, South Carolina (Souedan et al., 2021), Texas’s coastline (Bugica et al., 2020), and the Chesapeake Bay area (Zhang et al., 2018; Harding et al., 2019). Most notably, long-term water quality monitoring in the Chesapeake Bay has led to the identification of climatic and anthropogenic drivers for certain water quality parameters and subsequent evaluation of the effectiveness of past management and restoration efforts (Kemp et al., 2005; Leight et al., 2011; Zhang et al., 2018; Harding et al., 2019).Datasets used for prior longitudinal water quality studies are commonly a product of governmental agencies developing localized programs, like the Chesapeake Bay Program (Chesapeake Bay Monitoring Program, 2022), in response to increasing population and significant degradation of vital estuarine ecosystems. While national and regional efforts have attempted to provide unbiased, sustained monitoring, these programs currently lack the spatial extent needed to capture coastwide water quality trends. The National Estuarine Research Reserve System (NERRS) is one of the few organizations with dedicated coastal water quality monitoring stations, which are included as part of the NERRS System Wide Monitoring Program (SWMP) that maintains 355 coastal water quality monitoring stations across 29 designated coastal reserves along the USA coastline (National Estuarine Research Reserve System, 2022). Compared to the over 13,500 freshwater monitoring stations maintained by the United States Geological Survey (USGS, 2022), the relatively small number of water quality monitoring stations across coastal and estuarine waters (NOAA Tides & Currents, 2022; US EPA, 2022) are likely not representative of the variations in environmental conditions that we observe across the tens of thousands of miles of shoreline along the United States.Because of the limited number of unbiased monitoring programs, the ability to use water quality data from regulatory operations presents a potentially valuable resource for assessing long-term estuarine conditions. Regulatory programs differ from monitoring programs by collecting water quality samples to meet regulatory requirements and inform short-term decision-making. For example, in North Carolina (NC), there are four NERRS SWMP monitoring stations and eight coastal stations with water quality data available through the USGS (South Atlantic Water Science Center, North Carolina Office, 2022) and fifty stations from the NC Ambient Monitoring System (Water Quality Portal, 2021), but the NC Division of Marine Fisheries (NCDMF) shellfish sanitation program maintains 1,924 water quality monitoring stations. In fact, state shellfish sanitation programs across the USA collect an abundance of water quality observations, and often have for decades. Shellfish mariculture is highly dependent on water quality monitoring due to the direct influence that ambient conditions have on the safety of shellfish meat consumption. The U.S. Food and Drug Administration’s National Shellfish Sanitation Program (NSSP) was developed in 1925 to maintain public safety and human health standards in relation to the consumption of shellfish grown in potentially polluted waters (NSSP, 2019). The implementation of the NSSP has resulted in systematic sampling of water quality for day-to-day fisheries regulation, specifically for Fecal Indicator Bacteria (FIB), a group of bacteria that are commonly used as a proxy measure for harmful pathogen loads in the waterway that could potentially be incorporated into shellfish meat through filter feeding. Thus, fecal coliforms (FC), a type of FIB, and other environmental factors that contribute to FC load and water quality, are regularly measured in shellfish growing waters due to the food safety implications. As a product of this regular testing, fisheries operations have accumulated decades of data with the potential to provide insights on historical trends with wide spatial extents, potentially filling gaps in long-term water quality monitoring capacity.However, because of the limited resources and industry specific priorities, regulatory data can maintain underlying biases as a result of the sampling methodology used to collect the water quality sample. Often, the collection of a sample can be motivated by day-to-day operational decisions, such as weather, the availability of field technicians, and ease of collection. These operational decisions lead to non-random sampling that provides observations that are not always representative of the system’s true dynamics. Engaging regulatory personnel to understand their fisheries management and sampling decisions is necessary to properly analyze the observations collected by shellfish sanitation programs.For example, the NSSP permits states to employ one of two sampling strategies when collecting regulatory water quality data in shellfish growing waters: adverse pollution condition sampling and systematic random sampling. The adverse pollution condition sampling strategy describes sampling in periods when known contamination events (commonly due to point-source pollution events or rainfall events) have degraded the water quality, and data collected under these conditions capture peak contamination. States must collect “a minimum of five samples… annually under adverse pollution conditions from each sample station in the growing area” (NSSP, 2019) to meet NSSP sampling requirements. In contrast, the systematic random sampling strategy describes the collection of data across “a statistically representative cross section of all meteorological, hydrographic, and/or other pollution events” (NSSP, 2019), resulting in the data collection under varied environment and climactic conditions. For state programs that use systematic random sampling, the NSSP requires samples be collected at least 6 times throughout the year (NSSP, 2019). As a result of the requirements for the conditions under which the two systems of sampling can take place, the resulting data may be biased and impact their utility for use in long-term water quality assessments. With our growing reliance on aquaculture and the expanding value of shellfish production driving the development of fisheries management infrastructure (Azra et al., 2021), long-term datasets available through shellfish sanitation programs will become increasingly valuable. Realizing the potential of regulatory datasets to inform long-term water quality trends is a vital next step for assessing the health of our coastal ecosystems, but research is needed to determine the utility of these data for water quality analyses.The goal of this study was to utilize shellfish management data to infer long-term spatiotemporal trends in water quality parameters, including FC and salinity, while accounting for variation in routine sampling conditions and environmental landscapes. Study objectives included (1) analyzing spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, (2) identifying possible management and environmental drivers of fecal coliform trends, and (3) assessing the feasibility of using these monitoring data to infer long-term water quality dynamics. We focused on North Carolina’s shellfish waters as a representative study system due to the availability of public, digitized multidecadal data, and the region’s rapidly growing population, wide variety of land use characteristics along the coast, presence of the second largest estuarine system in the contiguous USA, and growing shellfish industry. Ultimately, this study demonstrates the application of shellfish management data for long-term water quality trend analysis in estuaries, informs future resource management strategies, and reveals new insights into the functioning of coastal systems.

Land-atmosphere feedbacks act through process chains that link variables in the land-atmosphere system. For the global energy and water cycles, the first link in the chain is soil moisture. Flux tower sites provide in-situ observations, including land surface states, surface fluxes, and nearsurface atmospheric states, to validate these links; however, they are unevenly distributed over the globe. Therefore, to obtain a global view of observationally based land-atmosphere coupling metrics, satellite data are useful. Among satellite products, the Soil Moisture Active Passive (SMAP) satellite provides the closest match to in-situ observations. However, SMAP exhibits stochastic random noise that can deflate coupling estimates. Since soil moisture variability closely follows a first-order Markov process, it typically has a distinct red noise spectrum. Satellite data with random noise has a whiter spectrum at high frequencies that can be compared to the expected red spectrum. Also, missing data in SMAP are not entirely random; its 8-day repeating polar orbit creates a cadence of missing data for both ascending and descending overpasses, depending on the location. This creates additional artifacts in the power spectrum, calculated through lagged autocovariance in the time series, with harmonic spikes at 8, 4 (8/2), 2 2/3 (8/3), and 2 (8/4) days that broaden due to the satellite's orbital variations. To be optimally useful for quantifying land-atmosphere feedbacks, the effects of random noise and periodic missing data must be minimized. A power spectrum adjustment technique has been designed to remove the orbital harmonic spikes from Level 3 (L3) SMAP data. This is achieved by fitting and removing a catenary function to the power spectrum between harmonic spikes. This adjusted spectrum is then scaled to match surface layer soil moisture observations at sites of the AmeriFlux network (in-situ data), which exhibit relatively low noise and have spectra that are very similar to those produced by offline land surface models (LSMs). Utilizing validated spectral data from gridded LSM-based datasets, a global L3 SMAP product with removed noise and harmonic effects is being produced. We will present results quantifying the extent to which this technique improves SMAP data and its temporal correlation with observations.