Geology topics

Jeanette K. Howard

Publications and source records attributed to Jeanette K. Howard.

6 recordsLinked to original sources

USGS2023

A decision-support framework for dam removal planning and its application in northern California

Dam removals are occurring more frequently with the rising cost of maintaining aging infrastructure, public safety concerns, and growing interest in river restoration. So far, most dam-removals have been unsystematic in their approach. Given the several thousand dam removals expected over the coming decades, a systematic approach to plan future dam removals holds potential for aligning and delivering multiple benefits. Despite multi-sector factors driving decision-making, most existing prioritization frameworks tend to operate within single or related disciplines. Here we present a hierarchical, multi-disciplinary decision-support framework to prioritize dam removals based on opportunistic factors (Tier 1), hydro-ecological variables (Tier 2), and socio-cultural considerations (Tier 3). This framework integrates multiple decision criteria under data availability constraints, incorporates value-driven weights, and can be applied to a portfolio of dams at various spatial scales. The final output facilitates the identification of dam removal projects that align opportunistic, environmental, and social benefits. We recommend the application of this framework as a critical first step to identifying high-priority candidates for removal, recognizing that removal decisions will ultimately require detailed feasibility studies and stakeholder engagement. To illustrate its utility, we apply this framework to California's North Coast region and identify a small number of “good” candidates to be considered for removal. We conclude with recommendations for filling critical knowledge gaps and advancing systematic dam removal planning in the United States and beyond.

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USGS2022

Modeling functional flows in California rivers

Environmental flows are critical to the recovery and conservation of freshwater ecosystems worldwide. However, estimating desired ranges of environmental flows across large, diverse landscapes is challenging. To advance protections of environmental flows for streams in California, USA, we developed a statewide modeling approach focused on functional components of the natural flow regime. Functional flow components in California streams—fall pulse flows, wet season peak flows and base flows, the spring flow recession, and dry season baseflows—support essential physical and ecological processes in riverine ecosystems. These functional flow components can be represented by functional flow metrics (FFMs) and quantified by their magnitude, timing, frequency, duration, and rate-of-change from daily streamflow records. After quantifying FFMs at reference-quality streamflow gages in California, we used machine-learning methods to estimate their natural range of values for all stream reaches in the state based on physical watershed characteristics and climatic factors. We found that the models performed well in predicting FFMs in streams across a diversity of landscape and climate contexts, according to several model performance criteria. Using the predicted FFM values, we established initial estimates of ecological flows that are expected to support critical functions and are broadly protective of ecosystem health. Modeling functional flows statewide offers a pathway for increasing the pace and scale of environmental flow protections in California and beyond.

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USGS2020

Characterizing benthic macroinvertebrate and algal biological condition gradient models for California wadeable Streams, USA

The Biological Condition Gradient (BCG) is a conceptual model that describes changes in aquatic communities under increasing levels of anthropogenic stress. The BCG helps decision-makers connect narrative water quality goals (e.g., maintenance of natural structure and function) to quantitative measures of ecological condition by linking index thresholds based on statistical distributions (e.g., percentiles of reference distributions) to expert descriptions of changes in biological condition along disturbance gradients. As a result, the BCG may be more meaningful to managers and the public than indices alone. To develop a BCG model, biological response to stress is divided into 6 levels of condition, represented as changes in biological structure (abundance and diversity of pollution sensitive versus tolerant taxa) and function. We developed benthic macroinvertebrate (BMI) and algal BCG models for California perennial wadeable streams to support interpretation of percentiles of reference-based thresholds for bioassessment indices (i.e., the California Stream Condition Index [CSCI] for BMI and the Algal Stream Condition Index [ASCI] for diatoms and soft-bodied algae). Two panels (one of BMI ecologists and the other of algal ecologists) each calibrated a general BCG model to California wadeable streams by first assigning taxa to specific tolerance and sensitivity attributes, and then independently assigning test samples (264 BMI and 248 algae samples) to BCG Levels 1–6. Consensus on the assignments was developed within each assemblage panel using a modified Delphi method. Panels then developed detailed narratives of changes in BMI and algal taxa that correspond to the 6 BCG levels. Consensus among experts was high, with 81% and 82% expert agreement within 0.5 units of assigned BCG level for BMIs and algae, respectively. According to both BCG models, the 10th percentiles index scores at reference sites corresponded to a BCG Level 3, suggesting that this type of threshold would protect against moderate changes in structure and function while allowing loss of some sensitive taxa. The BCG provides a framework to interpret changes in aquatic biological condition along a gradient of stress. The resulting relationship between index scores and BCG levels and narratives can help decision-makers select thresholds and communicate how these values protect aquatic life use goals.

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USGS2018

Sensitivity of streamflow to climate change in California

Climate change is rapidly altering the global water cycle, exposing vulnerabilities in both social and environmental systems. However, uncertainty in future climate predictions makes it difficult to design and evaluate strategies for building climate resilience. In regions such as California, characterized by stressed water-supply systems, high natural climate variability, and substantial uncertainty in future precipitation projections, alternative approaches to assessing climate risks may be useful. Here, we develop a hydrologic sensitivity approach to estimate regional streamflow responses to climate change in California. We use statistical models to predict monthly streamflow from physical catchment features and evaluate how flow changes with incremental changes in precipitation and temperature. The results indicate unique regional and monthly flow responses to climate change, with early summer flows (May - July) in interior mountain region having the greatest sensitivity to temperature and winter flow (December - March) in the xeric region having the greatest sensitivity to precipitation. When evaluated over the range of global climate model projections for mid-century (2040-2069), models generally suggest shifts in streamflow regimes towards higher wet season flows and lower dry season flows relative to historical conditions. The sensitivity analysis provides insight into catchment- and regional-scale hydrologic responses in California and complements other approaches for understanding the consequences of climatic change for water and risk management.

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USGS2017

Patterns and magnitude of flow alteration in California, USA

Quantifying the natural flow regime is essential for management of water resources and conservation of aquatic ecosystems. Understanding the degree to which anthropogenic activities have altered flows is critical for developing effective conservation strategies. Assessing flow alteration requires estimates of flows expected in the absence of human influence and under current land use and water management. There are several techniques to predict flows in streams and rivers; however, none have been applied to make predictions of natural flow conditions over large regions and time periods. We utilised machine learning statistical models to predict natural monthly flows (natural streamflows without the influence of water management or anthropogenic land use) in California from 1950 to 2015, using time-dependent and fixed watershed variables from reference stream gages. These models were then used to make estimates of mean, maximum and minimum monthly flows in all streams in the state. We compared observed flows measured at 540 stream gages across the state with expected natural flows at the same locations, to quantify the type, frequency and magnitude of flow alteration over the past 20 years (1996–2015). A gage was considered altered if an observed flow metric (monthly mean, annual maximum, annual minimum) fell outside the 80% prediction interval of the modelled flow estimate. We found that 95% of the 540 stream gages in California had at least 1 month of altered flows over the past 20 years, and 11% of gages were frequently altered (over two-thirds of the months recorded had evidence of altered flows). The type of alteration varied across the state with flows being either depleted, inflated or a mix of both at different times of the year. Most altered gages (68%) exhibited both depletion and inflation in monthly flows over the time period. Inflation of monthly mean flows was most prevalent during the summer months, while depletion of monthly flows was evident throughout the year. Type, frequency and magnitude of flow alteration varied by region. Flow depletion was present at >80% of gages in the North Coast and Central Coast, flow inflation was measured at >80% of gages in the South Coast and San Francisco Bay and both depletion and inflation were evident at >80% of gages in the Sacramento River and San Joaquin and Tulare regions. Annual maximum flows were consistently depleted and annual minimum flows were commonly inflated in the Sierra Nevada and Central Valley (Sacramento River and San Joaquin and Tulare regions). This is the first study to comprehensively assess flow alteration at stream gages across California. Understanding the patterns and degree of alteration can aid in prioritising streams for environmental flow assessment and developing conservation strategies for native freshwater biota.

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USGS2016

Estimating natural monthly streamflows in California and the likelihood of anthropogenic modification

Because natural patterns of streamflow are a fundamental property of the health of streams, there is a critical need to quantify the degree to which human activities have modified natural streamflows. A requirement for assessing streamflow modification in a given stream is a reliable estimate of flows expected in the absence of human influences. Although there are many techniques to predict streamflows in specific river basins, there is a lack of approaches for making predictions of natural conditions across large regions and over many decades. In this study conducted by the U.S. Geological Survey, in cooperation with The Nature Conservancy and Trout Unlimited, the primary objective was to develop empirical models that predict natural (that is, unaffected by land use or water management) monthly streamflows from 1950 to 2012 for all stream segments in California. Models were developed using measured streamflow data from the existing network of streams where daily flow monitoring occurs, but where the drainage basins have minimal human influences. Widely available data on monthly weather conditions and the physical attributes of river basins were used as predictor variables. Performance of regional-scale models was comparable to that of published mechanistic models for specific river basins, indicating the models can be reliably used to estimate natural monthly flows in most California streams. A second objective was to develop a model that predicts the likelihood that streams experience modified hydrology. New models were developed to predict modified streamflows at 558 streamflow monitoring sites in California where human activities affect the hydrology, using basin-scale geospatial indicators of land use and water management. Performance of these models was less reliable than that for the natural-flow models, but results indicate the models could be used to provide a simple screening tool for identifying, across the State of California, which streams may be experiencing anthropogenic flow modification.

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