Search for: All records

Abstract Wildfires are increasing globally in frequency, severity, and extent, but their impact on fluvial networks, and the resources they provide, remains unclear. We combine remote sensing of burn perimeter and severity, in-situ water quality monitoring, and longitudinal modeling to create the first large-scale, long-term estimates of stream+river length impacted by wildfire for the western US. We find that wildfires directly impact ~6% of the total stream+river length between 1984 and 2014, increasing at a rate of 342 km/year. When longitudinal propagation of water quality impacts is included, we estimate that wildfires affect ~11% of the total stream+river length. Our results indicate that wildfire activity is one of the largest drivers of aquatic impairment, though it is not routinely reported by regulatory agencies, as wildfire impacts on fluvial networks remain unconstrained. We identify key actions to address this knowledge gap and better understand the growing threat to fluvial networks, water security, and public health risks. 

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 Fluvial networks integrate, transform, and transport constituents from terrestrial and aquatic ecosystems. To date, most research on water quality dynamics has focused on process understanding at individual streams, and, as a result, there is a lack of studies analyzing how physical and biogeochemical drivers scale across fluvial networks. We performed tracer tests in five stream orders of the Jemez River continuum in New Mexico, USA, to quantify reach‐scale hyporheic exchange during two different seasonal periods to address the following: How do hyporheic zone contributions to overall riverine processing change with space and time? And does the spatiotemporal variability of hyporheic exchange scale across fluvial networks? Combining conservative (i.e., bromide) and reactive (i.e., resazurin) tracer analyses with solute transport modeling, we found a dominance of reaction‐limited transport conditions and a decrease of the contributions of hyporheic processing across stream orders and flow regimes. Our field‐based findings suggest that achieving knowledge transferability of hyporheic processing within fluvial networks may be possible, especially when process variability is sampled across multiple stream orders and flow regimes. Therefore, we propose a shift in our traditional approach to investigating scaling patterns in transport processes, which currently relies on the interpretation of studies conducted in multiple sites (mainly in headwater streams) that are located in different fluvial networks, to a more cohesive, network‐centered investigation of processes using the same or readily comparable methods. 

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. 

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.