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The increasing severity and frequency of wildfires in forested watersheds pose significant challenges to water quality management. This study examines the impacts of the 2022 Hermit's Peak-Calf Canyon gigafire, the largest wildfire in New Mexico's history. The wildfire burned over 1,382 km², affecting a key watershed that supplies drinking water to Las Vegas, NM. We conducted a longitudinal assessment of post-fire water quality dynamics across a 170 km fluvial network, analyzing flow, water quality parameters, nutrient and metal concentrations, and mobilization patterns. We found that post-fire nutrient concentrations exceeded pre-fire medians by up to two orders of magnitude. Our analyses revealed solute-specific transport patterns that are difficult to predict with static watershed- or fire-specific characteristics (e.g., burned area and percent severities). ${\text{NH}}_4^{+}$, ${\text{PO}}_3^{-}$, and ${\text{NO}}_2^{-}$were closely and positively associated with discharge and turbidity near the burn perimeter, while ${\text{NO}}_3^{-}$and TON exhibited strong mobilization trends ~170 km downstream. In contrast to nutrients, calcium, magnesium, and manganese levels showed no significant pre- vs. post-fire shifts, while concentrations of trace metals like Cr³⁺, Pb²⁺, Zn²⁺, and Sr²⁺surpassed background levels and public health thresholds. Our findings emphasize the significant propagation of wildfire disturbances over hundreds of kilometers and suggest the need for integrated watershed management strategies, including the management of large-scale flood control mechanisms to mitigate the far-reaching impacts of water quality disturbances post-fire. 

Despite advances in wastewater treatment plant (WWTP) efficiencies, multiple contaminants of concern, such as microplastics, pharmaceuticals, and per- and poly-fluoroalkyl substances (PFAS) remain largely untreated near discharge points and can be highly concentrated before they are fully mixed within the receiving river. Environmental agencies enforce mixing zone permits for the temporary exceedance of water quality parameters beyond targeted control levels under the assumption that contaminants are well-mixed and diluted downstream of mixing lengths, which are typically quantified using empirical equations derived from one-dimensional transport models. Most of these equations were developed in the 1970s and have been assumed to be standard practice since then. However, their development and validation lacked the technological advances required to test them in the field and under changing flow conditions. While new monitoring techniques such as remote sensing and infrared imaging have been employed to visualize mixing lengths and test the validity of empirical equations, those methods cannot be easily repeated due to high costs or flight restrictions. We investigated the application of Lagrangian and Eulerian monitoring approaches to experimentally quantify mixing lengths downstream of a WWTP discharging into the Rio Grande near Albuquerque, New Mexico (USA). Our data spans river to WWTP discharges ranging between 2-22x, thus providing a unique dataset to test long-standing empirical equations in the field. Our results consistently show empirical equations could not describe our experimental mixing lengths. Specifically, while our experimental data revealed “bell-shaped” mixing lengths as a function of increasing river discharges, all empirical equations predicted monotonically increasing mixing lengths. Those mismatches between experimental and empirical mixing lengths are likely due to the existence of threshold processes defining mixing at different flow regimes, i.e., jet diffusion at low flows, the Coanda effect at intermediate flows, and turbulent mixing at higher flows, which are unaccounted for by the one-dimensional empirical formulas. Our results call for a review of the use of empirical mixing lengths in streams and rivers to avoid widespread exposures to emerging contaminants. 

Anthropogenic and natural disasters (e.g., wildfires, oil spills, mine spills, sewage treatment facilities) cause water quality disturbances in fluvial networks. These disturbances are highly unpredictable in space-time, with the potential to propagate through multiple stream orders and impact human and environmental health over days to years. Due to challenges in monitoring and studying these events, we need methods to strategize the deployment of rapid response research teams on demand. Rapid response research has the potential to close the gap in available water quality data and process understanding through time-sensitive data collection efforts. This manuscript presents a protocol that can guide researchers in preparing for and researching water quality disturbance events. We tested and refined the protocol by assessing the longitudinal propagation of water quality disturbances from the 2022 Hermit's Peak—Calf Canyon, NM, USA, the largest in the state's recorded history. Our rapid response research allowed us to collect high-resolution water quality data with semi-continuous sensors and synoptic grab sampling. The data collected have been used for traditional peer-reviewed publications and pragmatically to inform water utilities, restoration, and outreach programs. 

Climate change is causing pronounced shifts during winter in the US, including shortening the snow season, reducing snowpack, and altering the timing and volume of snowmelt-related runoff. These changes in winter precipitation patterns affect in-stream freeze-thaw cycles, including ice and snow cover, and can trigger direct and indirect effects on in-stream physical, chemical, and biological processes in ~60% of river basins in the Northern Hemisphere. We used high-resolution, multi-parameter data collected in a headwater stream and its local environment (climate and soil) to determine interannual variability in physical, chemical, and biological signals in a montane stream during the winter of an El Niño and a La Niña year. We observed ~77% greater snow accumulation during the El Niño year, which caused the formation of an ice dam that shifted the system from a primarily lotic to a lentic environment. Water chemistry and stream metabolism parameters varied widely between years. They featured anoxic conditions lasting over a month, with no observable gross primary production (GPP) occurring under the ice and snow cover in the El Niño year. In contrast, dissolved oxygen and GPP remained relatively high during the winter months of the La Niña year. These redox and metabolic changes driven by changes in winter precipitation have significant implications for water chemistry and biological functioning beyond the winter. Our study suggests that as snow accumulation and hydrologic conditions shift during the winter due to climate change, hot-spots and hot-moments for biogeochemical processing may be reduced, with implications for the downstream movement of nutrients and transported materials. 

Abstract Sensor‐based, semicontinuous observations of water quality parameters have become critical to understanding how changes in land use, management, and rainfall‐runoff processes impact water quality at diurnal to multidecadal scales. While some commercially available water quality sensors function adequately under a range of turbidity conditions, other instruments, including those used to measure nutrient concentrations, cease to function in high turbidity waters (> 100 nephelometric turbidity units [NTU]) commonly found in large rivers, arid‐land rivers, and coastal areas. This is particularly true during storm events, when increases in turbidity are often concurrent with increases in nutrient transport. Here, we present the development and validation of a system that can affordably provide Self‐Cleaning FiLtrAtion for Water quaLity SenSors (SC‐FLAWLeSS), and enables long‐term, semicontinuous data collection in highly turbid waters. The SC‐FLAWLeSS system features a three‐step filtration process where: (1) a coarse screen at the inlet removes particles with diameter > 397 μm, (2) a settling tank precipitates and then removes particles with diameters between 10 and 397 μm, and (3) a self‐cleaning, low‐cost, hollow fiber membrane technology removes particles ≥ 0.2μm. We tested the SC‐FLAWLeSS system by measuring nitrate sensor data loss during controlled, serial sediment additions in the laboratory and validated it by monitoring soluble phosphate concentrations in the arid Rio Grande river (New Mexico, U.S.A.), at hourly sampling resolution. Our data demonstrate that the system can resolve turbidity‐related interference issues faced by in situ optical and wet chemistry sensors, even at turbidity levels > 10,000 NTU. 

Abstract We introduce “The Integrator,” a novel technique to quantify transport and reaction metrics commonly used to characterize flow systems. This development consists of two products: (1)The Integratorsampling device and (2) its supporting mathematical framework, which is compatible with semi‐continuous sensor data. The use ofThe Integratordevice simplifies the logistics of sample collection and greatly reduces the number of samples needed, making it ideal to characterize systems that are: (1) difficult to access, (2) large and thus intractable or highly heterogeneous, and (3) highly instrumented otherwise but where a more holistic, mechanistic understanding may be gained by monitoring one or more currently untracked elements. We tested and validatedThe Integratortechnique using experimental data collected from a heart rate monitor (high‐quality, high‐frequency data in response to known excitation events) and solute tracer experiments conducted in two contrasting (fourth and seventh order) rivers. In theSupporting Information, we provide details concerning the design ofThe Integratordevice used in our field case studies and provide insight into potential improvements. Despite our case studies focus on the analysis of conservative and reactive transport of solutes in rivers, the principles behindThe Integratortechnique can be used to monitor water quality in hyporheic zones, aquifers, wetlands, swamps, karsts, oceans, wastewater treatment plants, pipe networks, and air quality. Furthermore, special arrangements ofIntegratordevices can be used to gather data at spatial and temporal resolutions that are currently unattainable due to high transportation and/or personnel costs.