Geology topics

Ryan A. Hill

Publications and source records attributed to Ryan A. Hill.

6 recordsLinked to original sources

USGS2025

The US EPA’s National Nutrient Inventory: Critical shifts in US nutrient pollution sources from 1987 to 2017

Efforts to constrain the negative environmental impacts of excess nitrogen (N) and phosphorus (P) are costly and challenging, due in part to inconsistent reporting of nutrient sources at temporal and spatial scales relevant for local decision making. To meet this challenge, the U.S. Environmental Protection Agency’s National Nutrient Inventory provides estimates of major agricultural, urban, atmospheric, and natural nutrient fluxes for the contiguous United States at county and HUC12 scales annually from 1987 (from 1950 for agriculture) to 2017. Since the late 1980s, total N emissions and atmospheric N deposition have declined 22% and 15%, respectively, despite increased agricultural emissions. Over the same period, municipal wastewater N and P loads remained largely stable, despite population increases, through wastewater treatment upgrades and the phaseout of phosphorus-containing detergents. Improved agricultural efficiency allowed for dramatic increases in agricultural production and crop harvest since 1987 (∼25% for N and P), with little change in surplus nutrients left on fields. Overall, a combination of innovative technologies and management has stemmed or even decreased major sources of nutrient pollution to the environment over the last several decades, representing an important shift that, if continued, may contribute to improved air, land, and water quality and human health.

contiguous United States↗

USGS2024

Improving ecosystem health in highly altered river basins: A generalized framework and its application to the Mississippi-Atchafalaya River Basin

Continued large-scale public investment in declining ecosystems depends on demonstrations of “success”. While the public conception of “success” often focuses on restoration to a pre-disturbance condition, the scientific community is more likely to measure success in terms of improved ecosystem health. Using a combination of literature review, workshops and expert solicitation we propose a generalized framework to improve ecosystem health in highly altered river basins by reducing ecosystem stressors, enhancing ecosystem processes and increasing ecosystem resilience. We illustrate the use of this framework in the Mississippi-Atchafalaya River Basin (MARB) of the central United States (U.S.), by (i) identifying key stressors related to human activities, and (ii) creating a conceptual ecosystem model relating those stressors to effects on ecosystem structure and processes. As a result of our analysis, we identify a set of landscape-level indicators of ecosystem health, emphasizing leading indicators of stressor removal (e.g., reduced anthropogenic nutrient inputs), increased ecosystem function (e.g., increased water storage in the landscape) and increased resilience (e.g., changes in the percentage of perennial vegetative cover). We suggest that by including these indicators, along with lagging indicators such as direct measurements of water quality, stakeholders will be better able to assess the effectiveness of management actions. For example, if both leading and lagging indicators show improvement over time, then management actions are on track to attain desired ecosystem condition. If, however, leading indicators are not improving or even declining, then fundamental challenges to ecosystem health remain to be addressed and failure to address these will ultimately lead to declines in lagging indicators such as water quality. Although our model and indicators are specific to the MARB, we believe that the generalized framework and the process of model and indicator development will be valuable in an array of altered river basins.

Mississippi-Atchafalaya River Basin↗

USGS2024

Quantifying form resistance is essential for estimating summer low and bankfull flow from stream survey channel morphology

Reliable estimates of low flow and flood discharge at ungaged locations are required for evaluating stream flow alteration, designing culverts and stream crossings, and interpreting regional surveys of habitat and biotic condition. Very few stream gaging stations are located on small, remote streams, which typically have complex channel morphology. Adequate gaging is also lacking on larger streams that are remote, smaller than those typically gaged, or have channel morphology not conducive to installation of gages. Complex channels typically contain large scale hydraulic roughness elements that dominate flow patterns (i.e., form roughness), making it difficult to measure channel cross-section area and water velocity, or to measure channel volume even where discharge is known. In channels with large channel form roughness, it is equally difficult to estimate discharge using commonly applied equations based on slope and channel dimensions or basin area. We employed a novel approach that explicitly accounts for hydraulic resistance from large wood and riffle-pool morphology (form roughness) in calculating low flow and bankfull discharge from stream and river physical habitat data collected from 4229 stream and river sites in the conterminous US (CONUS) sampled in 2008–9 and 2013–14 as part of the US Environmental Protection Agency's N ational R ivers and S treams A ssessment (NRSA). Hydraulic resistance derived from form roughness clearly dominated resistance derived from bed particles (particle resistance) during summer low flows in wadeable streams across the spectrum of channel slopes and substrate sizes smaller than boulders. Under bankfull conditions, the influence of form resistance relative to particle resistance was diminished, but form resistance still dominated except in large low gradient rivers lacking complex channels, and in streams or rivers with boulder-size bed particles. We validated our hydraulic resistance estimates by comparing measured discharges with calculated discharges that used those hydraulic resistance estimates along with measured NRSA channel morphology data. Morphology-based summer discharge (low flow) estimates and direct field measurements of discharge in 2333 wadeable CONUS streams showed reasonable agreement (median difference < 2.5×) for discharges ranging from 3.6 × 10 −5 to 123 m 3 /s and drainage areas of 0.12 to 171,000 km 2 . In a subset of 759 of NRSA's larger wadeable stream and non-wadeable river sites where nearby U.S. Geological Survey (USGS) gage data were available and adequate, our morphology-based summer discharge estimates agreed fairly well (median difference < 2.0×) with USGS 20-yr average August mean flows ranging from 0.003 to 16,000 m 3 /s. Similarly, morphology-based estimates of bankfull flow ranging from 0.3 to 100,000 m 3 /s agreed reasonably well with the 1.5-yr recurrence interval flood in these gaged sites (median deviation <2.2×). These findings demonstrate the importance of quantifying flow resistance from large-scale form roughness features in natural channels and provide a novel approach for estimating discharge from widely available survey data. This will allow examination of discharge and its ecological influence across the full range of stream and river sizes sampled by NRSA or other synoptic surveys where comprehensive measures of biota, physical habitat, and chemistry are also made. Although these morphology-based estimates exhibit some variability, they are adequate for examining regional patterns in discharge and flow alteration and their association with instream biota and anthropogenic disturbances, providing summer low and bankfull flow information where reliable estimates are lacking.

Geomorphology↗

USGS2023

Density declines, richness increases, and composition shifts in stream macroinvertebrates

Documenting trends of stream macroinvertebrate biodiversity is challenging because biomonitoring often has limited spatial, temporal, and taxonomic scopes. We analyzed biodiversity and composition of assemblages of >500 genera, spanning 27 years, and 6131 stream sites across forested, grassland, urban, and agricultural land uses throughout the United States. In this dataset, macroinvertebrate density declined by 11% and richness increased by 12.2%, and insect density and richness declined by 23.3 and 6.8%, respectively, over 27 years. In addition, differences in richness and composition between urban and agricultural versus forested and grassland streams have increased over time. Urban and agricultural streams lost the few disturbance-sensitive taxa they once had and gained disturbance-tolerant taxa. These results suggest that current efforts to protect and restore streams are not sufficient to mitigate anthropogenic effects.

Science Advances↗

USGS2022

Vulnerable waters are essential to watershed resilience

Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management.

Ecosystems↗

USGS2013

Predicting thermal reference conditions for USA streams and rivers

Temperature is a primary driver of the structure and function of stream ecosystems. However, the lack of stream temperature (ST) data for the vast majority of streams and rivers severely compromises our ability to describe patterns of thermal variation among streams, test hypotheses regarding the effects of temperature on macroecological patterns, and assess the effects of altered STs on ecological resources. Our goal was to develop empirical models that could: 1) quantify the effects of stream and watershed alteration (SWA) on STs, and 2) accurately and precisely predict natural (i.e., reference condition) STs in conterminous USA streams and rivers. We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual ST. To build reference-condition models (RCMs), we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. We first created a set of dirty models (DMs) that related STs to both natural factors (e.g., climate, watershed area, topography) and measures of SWA, i.e., reservoirs, urbanization, and agriculture. The 3 models performed well (r 2 = 0.84–0.94, residual mean square error [RMSE] = 1.2–2.0 ° C). For each DM, we used partial dependence plots to identify SWA thresholds below which response in ST was minimal. We then used data from only the sites with upstream SWA below these thresholds to build RCMs with only natural factors as predictors (r 2 = 0.87–0.95, RMSE = 1.1–1.9 ° C). Use of only reference-quality sites caused RCMs to suffer modest loss of predictor space and spatial coverage, but this loss was associated with parts of ST response curves that were flat and, therefore, not responsive to further variation in predictor space. We then compared predictions made with the RCMs to predictions made with the DMs with SWA set to 0. For most DMs, setting SWAs to 0 resulted in biased estimates of thermal reference condition.

Freshwater Science↗