Seasonal Groundwater Level Dynamics in Unconfined Aquifers across the United States.

Groundwater hydrographs contain a rich set of information on the dynamics of aquifer systems and the processes and properties that influence them. While the importance of seasonal cycles in hydrologic and environmental state variables is widely recognized there has yet to be a comprehensive analysis of the seasonal dynamics of groundwater across the United States. Here we use time series of groundwater level measurements from 997 wells from the National Groundwater Monitoring Network to identify and describe groundwater seasonal cycles in unconfined aquifers across the United States. We use functional data analysis to obtain a functional form fit for each site and apply an unsupervised clustering algorithm to identify a set of five distinct seasonal cycles regimes. Each seasonal cycle regime has a distinctive shape and distinct timing of its annual minimum and maximum water level. There are clear spatial patterns in the occurrence of each seasonal cycle regime, with the relative occurrence of each regime strongly influenced by the geologic setting (aquifer system), climate, and topography. Our findings provide a comprehensive characterization of groundwater seasonal cycles across much of the United States and present both a methodology and results useful for assessing and understanding unconfined groundwater systems.

Automatic selection of the number of clusters using Bayesian clustering and sparsity-inducing priors.

Clustering is a ubiquitous task in ecological and environmental sciences and multiple methods have been developed for this purpose. Because these clustering methods typically require users to a priori specify the number of groups, the standard approach is to run the algorithm for different numbers of groups and then choose the optimal number using a criterion (e.g., AIC or BIC). The problem with this approach is that it can be computationally expensive to run these clustering algorithms multiple times (i.e., for different numbers of groups) and some of these information criteria can lead to an overestimation of the number of groups. To address these concerns, we advocate for the use of sparsity-inducing priors within a Bayesian clustering framework. In particular, we highlight how the truncated stick-breaking (TSB) prior, a prior commonly adopted in Bayesian nonparametrics, can be used to simultaneously determine the number of groups and estimate model parameters for a wide range of Bayesian clustering models without requiring the fitting of multiple models. We illustrate the ability of this prior to successfully recover the true number of groups for three clustering models (two types of mixture models, applied to GPS movement data and species occurrence data, as well as the species archetype model) using simulated data in the context of movement ecology and community ecology. We then apply these models to armadillo movement data in Brazil, plant occurrence data from Alberta (Canada), and bird occurrence data from North America. We believe that many ecological and environmental sciences applications will benefit from Bayesian clustering methods with sparsity-inducing priors given the ubiquity of clustering and the associated challenge of determining the number of groups. Two R packages, EcoCluster and bayesmove, are provided that enable the straightforward fitting of these models with the TSB prior.

Well-Switching to Reduce Arsenic Exposure in Bangladesh: Making the Most of Inaccurate Field Kit Measurements.

Well-switching programs in Bangladesh have successfully lowered arsenic exposure. In these programs, households switch from wells that are labeled "unsafe" to nearby wells labeled "safe," but these designations are usually based on inherently inaccurate field kit measurements. Here, we (a) compare the efficacy of field-kit measurements to accurate laboratory measurements for well switching, (b) investigate the potential impact on well switching of the chosen "safe" threshold, and (c) consider the possible benefits of providing more detailed concentration information than just "safe" and "unsafe." We explore different hypothetical mitigation scenarios by combining two extensive data sets from Araihazar Bangladesh: a blanket survey of 6595 wells over 25 km2 based on laboratory measurements and 943 paired kit and laboratory measurements from the same area. The results indicate that the decline in average arsenic exposure from relying on kit rather than laboratory data is modest in relation to the logistical and financial challenge of delivering exclusively laboratory data. The analysis further indicates that the 50 µg/L threshold used in Bangladesh to distinguish safe and unsafe wells, rather than the WHO guideline of 10 µg/L, is close to optimal in terms of average exposure reduction. We also show that providing kit data at the maximum possible resolution rather than merely classifying wells as unsafe or safe would be even better. These findings are relevant as the government of Bangladesh is about to launch a new blanket testing campaign of millions of wells using field kits.

The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests.

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long-term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small-sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high-dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.

COVID-19 and climatic factors: A global analysis.

BACKGROUND: It is unknown if COVID-19 will exhibit seasonal pattern as other diseases e.g., seasonal influenza. Similarly, some environmental factors (e.g., temperature, humidity) have been shown to be associated with transmission of SARS-CoV and MERS-CoV, but global data on their association with COVID-19 are scarce. OBJECTIVE: To examine the association between climatic factors and COVID-19. METHODS: We used multilevel mixed-effects (two-level random-intercepts) negative binomial regression models to examine the association between 7- and 14-day-lagged temperature, humidity (relative and absolute), wind speed and UV index and COVID-19 cases, adjusting for Gross Domestic Products, Global Health Security Index, cloud cover (%), precipitation (mm), sea-level air-pressure (mb), and daytime length. The effects estimates are reported as adjusted rate ratio (aRR) and their corresponding 95% confidence interval (CI). RESULTS: Data from 206 countries/regions (until April 20, 2020) with ≥100 reported cases showed no association between COVID-19 cases and 7-day-lagged temperature, relative humidity, UV index, and wind speed, after adjusting for potential confounders, but a positive association with 14-day-lagged temperature and a negative association with 14-day-lagged wind speed. Compared to an absolute humidity of <5 g/m3, an absolute humidity of 5-10 g/m3 was associated with a 23% (95% CI: 6-42%) higher rate of COVID-19 cases, while absolute humidity >10 g/m3 did not have a significant effect. These findings were robust in the 14-day-lagged analysis. CONCLUSION: Our results of higher COVID-19 cases (through April 20) at absolute humidity of 5-10 g/m3 may be suggestive of a 'sweet point' for viral transmission, however only controlled laboratory experiments can decisively prove it.

Spatial variation in the detection rates of frequently studied pharmaceuticals in Asian, European and North American rivers.

Pharmaceutical consumption has expanded rapidly during the last century and their persistent presence in the environment has become a major concern. Unfortunately, our understanding of the distribution of pharmaceuticals in surface water and their effects on aquatic biota and public health is limited. Here, we explore patterns in the detection rate of the most frequently studied pharmaceuticals in 64 rivers from 22 countries using bi-clustering algorithms and subsequently analyze the results in the context of regional differences in pharmaceutical consumption habits, social and environmental factors, and removal-efficiency of wastewater treatment plants (WWTP). We find that 20% of the pharmaceuticals included in this analysis are pervasively present in all the surface waterbodies. Several pharmaceuticals also display low overall positive detection rates; however, they exhibit significant spatial variability and their detection rates are consistently lower in Western European and North America (WEOG) rivers in comparison to Asian rivers. Our analysis suggests the important role of pharmaceutical consumption and population in governing these patterns, however the role of WWTP efficiency appeared to be limited. We were constrained in our ability to assess the role of hydrology, which most likely also plays an important role in regulating pharmaceuticals in rivers. Most importantly though, we demonstrate the ability of our algorithm to provide probabilistic estimates of the detection rate of pharmaceuticals that were not studied in a river, an exercise that could be useful in prioritizing pharmaceuticals for future study.

Persistent Urban Influence on Surface Water Quality via Impacted Groundwater.

Growing urban environments stress hydrologic systems and impact downstream water quality. We examined a third-order catchment that transitions from an undisturbed mountain environment into urban Salt Lake City, Utah. We performed synoptic surveys during a range of seasonal baseflow conditions and utilized multiple lines of evidence to identify mechanisms by which urbanization impacts water quality. Surface water chemistry did not change appreciably until several kilometers into the urban environment, where concentrations of solutes such as chloride and nitrate increase quickly in a gaining reach. Groundwater springs discharging in this gaining system demonstrate the role of contaminated baseflow from an aquifer in driving stream chemistry. Hydrometric and hydrochemical observations were used to estimate that the aquifer contains approximately 18% water sourced from the urban area. The carbon and nitrogen dynamics indicated the urban aquifer also serves as a biogeochemical reactor. The evidence of surface water-groundwater exchange on a spatial scale of kilometers and time scale of months to years suggests a need to evolve the hydrologic model of anthropogenic impacts to urban water quality to include exchange with the subsurface. This has implications on the space and time scales of water quality mitigation efforts.

Stable hydrogen and oxygen isotopes of tap water reveal structure of the San Francisco Bay Area's water system and adjustments during a major drought.

Water availability and sustainability in the Western United States is a major flashpoint among expanding communities, growing industries, and productive agricultural lands. This issue came to a head in 2015 in the State of California, when the State mandated a 25% reduction in urban water use following a multi-year drought that significantly depleted water resources. Water demands and challenges in supplying water are only expected to intensify as climate perturbations, such as the 2012-2015 California Drought, become more common. As a consequence, there is an increased need to understand linkages between urban centers, water transport and usage, and the impacts of climate change on water resources. To assess if stable hydrogen and oxygen isotope ratios could increase the understanding of these relationships within a megalopolis in the Western United States, we collected and analyzed 723 tap waters across the San Francisco Bay Area during seven collection campaigns spanning 21 months during 2013-2015. The San Francisco Bay Area was selected as it has well-characterized water management strategies and the 2012-2105 California Drought dramatically affected its water resources. Consistent with known water management strategies and previously collected isotope data, we found large spatiotemporal variations in the δ2H and δ18O values of tap waters within the Bay Area. This is indicative of complex water transport systems and varying municipality-scale management decisions. We observed δ2H and δ18O values of tap water consistent with waters originating from snowmelt from the Sierra Nevada Mountains, local precipitation, ground water, and partially evaporated reservoir sources. A cluster analysis of the isotope data collected in this study grouped waters from 43 static sampling sites that were associated with specific water utility providers within the San Francisco Bay Area and known management practices. Various management responses to the drought, such as source switching, bringing in new sources, and water conservation, were observed in the isotope data. Finally, we estimated evaporative loss from one utility's reservoir system during the 2015 water year using a modified Craig-Gordon model to estimate the consequences of the drought on this resource. We estimated that upwards of 6.6% of the water in this reservoir system was lost to evaporation.

Urban water - a new frontier in isotope hydrology.

Isotope hydrology has focused largely on landscapes away from densely inhabited regions. In coming decades, it will become increasingly more important to focus on water supplies and dynamics within urban systems. Stable isotope analyses provide important information to water managers within large cities, particularly in arid regions where evaporative histories of water sources, vulnerabilities, and reliabilities of the water supplies can be major issues. Here the spatial and vertical understanding of water supporting urban systems that comes from stable isotope analyses can serve as a useful management tool. We explore this research frontier using the coupled natural-human landscape of the Salt Lake Valley, USA, with its greater than one million inhabitants. We first provide data on the stable isotope ratios of the hydrologic system's primary components: precipitation, incoming surface waters, and terminus waters in this closed basin. We then explore the spatial and temporal patterns of drinking waters within the urban landscape and the new opportunities to better link isotope ratio data with short- and long-term management interests of water managers.