Geology topics

Nicholas A Som

Publications and source records attributed to Nicholas A Som.

4 recordsLinked to original sources

USGS2026

Rewilding the upper Klamath River Basin: Rapid recolonization of Chinook salmon following the world’s largest dam removal

Habitat fragmentation is a global driver of decline in biodiversity, yet evaluations of ecological response following re-connection remain rare. In 2024, four hydroelectric dams on the Klamath River were removed, reconnecting over 640 km of anadromous habitat and marking the largest dam removal project completed to date. We used a stationary high-resolution multi-beam imaging SONAR to quantify adult Chinook salmon ( Oncorhynchus tshawytscha ) passage into newly reopened reaches during the first two migratory seasons following dam removal. An estimated 7,742 (95% CI: 7,702–7,778) Chinook salmon migrated upstream in 2024 and 13,310 (95% CI: 12,876–13,733) in 2025, representing 18–19% of fish returning to the Klamath River Basin. Within this period, Chinook salmon recolonized 88% of their documented historical distribution, expanding into 345 km of reconnected habitats and reaching elevations of up to 1,250 m. Recolonization required fish to swim through dam removal sites, complex hydraulic corridors and engineered fishways, demonstrating the propensity for rapid recolonization at basin scale despite these challenges. These findings provide the first quantitative assessment of salmon response to the world’s largest dam removal and demonstrate how reconnection can catalyze large-scale recolonization in historically fragmented river networks.

California, Oregon↗

USGS2026

A method to implement natural flow regimes for regulated rivers

Rivers throughout the world have been dammed for flood control, irrigation, hydropower, and water storage for centuries. Dams service the economic and development needs of societies, but degrade the ecology of rivers. To conserve diminishing aquatic species and their habitats, methods are needed to help managers implement flow releases with timescales and patterns that are relevant to aquatic species and physical processes. We present a method to synchronize flow releases from dams with local hydrology, while ensuring constraints of flood control and water allocations are met. We demonstrate an optimization technique that scales flows of a proximal unregulated river and results in discharges that are synchronized with natural hydrologic patterns. Importantly, our method preserves operational and regulatory requirements such as water allocation volumes and flood control thresholds. This technique can be used at various timescales, and can incorporate many system-specific constraints. We selected the Trinity River, California as an example, and scale flows according to a proximal gage on the Salmon River in California. We evaluate our technique using an array of flow regime metrics from the literature, and our method produced hydrographs with greater flow variation throughout a larger portion of the water year than current management strategies. These hydrographs yielded flow metrics more similar to those from unimpaired flow patterns, including those measuring within and among year flow variability. The Real Time Management framework is a new tool that can be implemented to improve the health of rivers and the aquatic species that depend upon them.

California↗

USGS2026

Discharge magnitude drives interannual variation in distribution of the invertebrate host Manayunkia occidentalis of the salmon parasite Ceratonova shasta

Objective The objective of this study was to evaluate statistical evidence for relationships between flow event characteristics and annelid host distribution, an important and necessary step toward providing and justifying management actions. Declines in Klamath River salmon have been attributed to infection and disease that are caused by the myxozoan parasite Ceratonova shasta . Flow manipulation has been used to manage risk of C. shasta for juvenile salmonids in this system. One mechanism by which flow-related disturbance can reduce the risk of C. shasta infection for salmon is by reducing populations of the obligate invertebrate host Manayunkia occidentalis . We previously demonstrated that hydraulic conditions during peak discharge events drive the distribution of M. occidentalis and suggested that high-magnitude flow events would be effective for reducing the distribution of the annelid host. However, evidence of the effects of flow events on M. occidentalis was needed to support the use of flow management. Methods To address this knowledge gap, we leveraged a multiyear data set to estimate relationships between the distribution the annelid host and the characteristics of each discharge event. We measured the presence or absence of M. occidentalis at spatially georeferenced sampling locations, stratified across the range of hydraulic and substrate conditions in three reaches annually from 2012 to 2020. Results During the study period, the magnitude of peak discharge ranged from 52.4 to 314.3 m 3 /s. The inclusion of seven additional years’ data in the base model (hierarchical; annual-level covariate + previous “single year” model) resulted in parameter estimates that were similar to those that were derived for the previous (2016) model, supporting that substrate, depth, and velocity during peak discharge predicted annelid distribution. The magnitude model (base model + annual-level covariate for peak discharge) showed evidence of a strong negative association with annelid presence (95% of all Markov chain–Monte Carlo draws were negative). Every 101.9 m 3 /s increase in peak discharge was associated with an estimated 39% decrease in the odds of annelid presence. The estimated effect of magnitude on the probability of annelid presence was illustrated in the contrast between low- and high-magnitude peak discharge scenarios. For both scenarios, the effects of depth, velocity at peak discharge, and substrate, consistent with the previous model (2016), were evident, as was the further reduction in probability of annelid presence at the higher peak discharge. However, under the low-magnitude discharge scenario, M. occidentalis were predicted to be present on smaller less stable substrates and at a wider range of depths and velocities than they were under the high-magnitude discharge scenario. In contrast to the covariate for magnitude, the inclusion of a duration covariate did not show a strong relationship with annelid distribution, which we attributed to the lack of variation in this covariate during the study period. Conclusions This work provides quantitative evidence that high-magnitude peak discharge flow events lead to lower probabilities of annelid host presence. Lower probabilities of annelid hosts are indicative of a reduced distribution of the M. occidentalis host and in turn reduced risk of C. shasta for salmon.

California↗

USGS2026

Evaluating alternative methods for modeling trap efficiencies of out-migrating juvenile salmonids

Objective We aimed to compare two machine learning approaches—boosted beta regression (BBR) and beta mixed model forest (BMF)—to a Bayesian mixed-effects beta regression (BME) for the prediction of rotary screw trap (RST) efficiency for out-migrating juvenile salmonids from environmental covariates. Methods We identified two machine learning approaches that shared the ability to model overdispersed probabilities. We compared the BBR and BMF machine learning models to a BME model to evaluate precision in detection probability prediction and model performance on bias in parameter estimation. We tested our three candidate models using a simulation study to understand the specific advantages and disadvantages of each when the data set was increasingly sparse and the capture probabilities were realistically small. We then applied the models to a case study of RST data from the Klamath River in California, United States. Results The BME and BMF outperformed BBR in all simulated scenarios, although the BMF displayed poor explanatory power. In the case study, the BME and BMF identified environmental covariates that predicted RST efficiency. Conclusions Using the BME as a benchmark for comparing machine learning approaches to trap efficiency modeling, our simulations and case study demonstrated that the BMF performed well and is a viable modeling approach with strong predictive power. The BME model would be the preferred modeling approach when its strong explanatory power is desired.

North American Journal of Fisheries Management↗