Geology topics

Timothy O. Hodson

Publications and source records attributed to Timothy O. Hodson.

14 recordsLinked to original sources

USGS2025

Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States

Hydrologic research and operations make extensive use of streamflow time series. In most applications, these time series are estimated from rating curves, which relate flow to some easy-to-measure surrogate, typically stage. The conventional stage-discharge rating takes the form of a segmented power law, with one segment for each hydrologic control at the stream gauge. However, these ratings are notoriously difficult to estimate with numerical methods, so that most are still developed manually. A few automated algorithms have emerged, but their use is sporadic, and their relative merits have not been rigorously assessed. One recently developed approach, the generalized power-law, avoids the segmenting problem by representing the power-law exponent as a Gaussian process. On the one hand, this representation is more flexible and easier to fit, but its flexibility might allow unrealistic solutions, so it needs to be tested under a range of conditions to assess its operational viability. This study evaluates the generalized power-law rating curve model by applying it to observations from 180 streams in Iceland, Sweden, and the United States. Overall, the model proved flexible and computationally robust, generating convincing rating curves across a range of geographic settings and was comparable to curves generated by a segmented rating model. Lastly, we propose a model-selection algorithm based on information theory to help identify the best rating curve model for a particular stream gauge.

Journal of Hydrology↗

USGS2024

Evaluating hydrologic model performance for characterizing streamflow drought in the conterminous United States

Hydrologic models are the primary tools that are used to simulate streamflow drought and assess impacts. However, there is little consensus about how to evaluate the performance of these models, especially as hydrologic modeling moves toward larger spatial domains. This paper presents a comprehensive multi-objective approach to systematically evaluating the critical features in streamflow drought simulations performed by two widely used hydrological models. The evaluation approach captures how well a model classifies observed periods of drought and non-drought, quantifies error components during periods of drought, and assesses the models’ simulations of drought severity, duration, and intensity. We apply this approach at 4662 U.S. Geological Survey streamflow gages covering a wide range of hydrologic conditions across the conterminous U.S. from 1985 to 2016 to evaluate streamflow drought using two national-scale hydrologic models: the National Water Model (NWM) and the National Hydrologic Model (NHM); therefore, a benchmark against which to evaluate additional models is provided. Using this approach, we find that generally the NWM better simulates the timing of flows during drought, while the NHM better simulates the magnitude of flows during drought. Both models performed better in wetter eastern regions than in drier western regions. Finally, each model showed increased error when simulating the most severe drought events.

conterminous United States↗

USGS2024

How to select an objective function using information theory

In machine learning or scientific computing, model performance is measured with an objective function. But why choose one objective over another? According to the information-theoretic paradigm, the “best” objective function is whichever minimizes information loss. To evaluate different objectives, transform them into likelihoods. The ratios of these likelihoods represent how strongly we should prefer one objective versus another, and the log of that ratio represents the relative information loss (or gain) from one objective to another. In plain terms, minimizing information loss is equivalent to minimizing uncertainty, as well as maximizing probability and general utility. We argue that this paradigm is well-suited to models that have many uses and no definite utility like the complex Earth system models used to understand the effects of climate change. Furthermore, the benefits of “maximizing information and general utility” extend beyond model accuracy to other important considerations including how efficiently the model calibrates, how well it generalizes, and how well it compresses data.

Water Resources Research↗

USGS2024

Ratingcurve: A Python package for fitting streamflow rating curves

Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure proxy like water surface elevation (stage). Despite the existence of automated methods, most rating curves are still fit manually, which can be time-consuming and subjective. Although several automated methods exist, they vary greatly in performance because of the non-convex nature of the problem. In this work, we develop a parameterization of the segmented power law that works reliably with minimal data, which could serve operationally or as a benchmark for evaluating other methods. The model, along with test data and tutorials, is available as an open-source Python package called ratingcurve . The implementation uses a modern probabilistic machine-learning framework, which is relatively easy to modify so that others can improve upon it.

Hydrology↗

USGS2023

Reproducibility starts at the source: R, Python, and Julia Packages for retrieving USGS hydrologic data

Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users.

Water↗

USGS2023

Nitrate-nitrogen and total phosphorus river loads

Nitrate-nitrogen and total phosphorus loads from the major rivers draining Illinois were updated through the 2021 water year (Figure 3.1). Beginning with the 2023 biennial update to the Illinois NLRS, nutrient loads were estimated using data from the U.S. Geological Survey continuous monitoring stations rather than the original Illinois EPA monitoring stations. To maintain consistency with previous biennial report updates, the loads from the new sites were rescaled to match the original drainage areas (Table 3.1). As before, the statewide nutrient load was estimated by summing the loads of the eight major rivers adjusting for out-of-state contributions and unmonitored areas.

Illinois↗

USGS2022

Spatial and temporal variations in phosphorus loads in the Illinois River Basin, Illinois USA

Total phosphorus (TP) loads in many rivers in the north-central United States have increased, including the Illinois River at Valley City, Illinois, USA, which increased 39% from the periods 1989–1996 to 2015–2019 despite efforts to reduce loads from point and nonpoint sources. Here, we quantify long-term variations in phosphorus (P) loads in the Illinois River and its tributaries and identify factors that may be causing the variations. We calculated river loads of dissolved (DP) and particulate P (PP), total and volatile suspended solids (TSS and VSS), and other potentially related constituents at 41 locations. DP loads generally increased and PP and TSS loads generally decreased from 1989–1996 to 2015–2019. During 1989–1996, P accumulated in the lower basin between Marseilles and Valley City (excluding monitored tributaries). This portion of the basin is very flat and accumulates sediment. During 2015–2019, this section shifted from being a net sink to being a net source of P, accounting for 78% of the increased TP load at Valley City. We present evidence supporting several mechanisms that could have caused this shift: increased DP and chloride loads, reduced sulfate and nitrate concentrations influencing ionic strength and redox potential in the sediments, and increased VSS loads at Valley City possibly indicating greater algal production and contributing to hypoxia in lower river sediments. Additional research is needed to quantify the relative importance of these mechanisms.

Illinois, Indiana, Wisconsin↗

USGS2022

Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not

The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.

Geoscientific Model Development↗

USGS2022

Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20

The Des Plaines River in southern Wisconsin and northern Illinois is the principal conduit for the discharge of wastewater effluent and stormwater runoff from the greater Chicago metropolitan area. In November 2017, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, installed a continuous monitoring station to measure water quality and streamflow in the Des Plaines River at Joliet, Illinois. Surrogate models encompassing continuous data and discrete water-quality samples were used to estimate loads of nitrate, total phosphorus, and suspended sediment. Comparisons to other major rivers in Illinois show that the Des Plaines River is a substantial contributor to statewide loading estimates for nitrate and total phosphorus but only a minor contributor to suspended sediment. Future loading estimates of total phosphorus could include more research into the effects of combined sewage overflows because these effects likely increased model uncertainty. The results in this report document current loadings and provide a baseline from which to assess future water-quality management decisions.

Illinois↗

USGS2021

Mean squared error, deconstructed

As science becomes increasingly cross-disciplinary and scientific models become increasingly cross-coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like “season” or “variability,” and that simple decompositions can be combined to represent more complex concepts, like “seasonal variability,” creating an expressive language through which to interrogate models and data.

Journal of Advances in Earth Systems Modeling↗

USGS2021

Continuous monitoring and Bayesian estimation of nutrient and sediment loads from Illinois watersheds, for water years 2016–2020

The State of Illinois is one of the leading contributors of nitrogen, phosphorus, and suspended sediment to the Mississippi River and the Gulf of Mexico. During water years 2016–20, the U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, operated continuous monitoring stations on eight major rivers in Illinois to better quantify nutrient and sediment loadings from the State of Illinois to the Mississippi River. This report estimates nitrate, phosphorus, and suspended-sediment loadings over that period, which can provide a benchmark against which to assess future changes in loading. In addition, this report develops a new method for incorporating the uncertainty created by gaps in continuous datasets based on Bayesian machine learning. Data gaps are a common problem in continuous monitoring, and gap filling is necessary to quantify loadings and the uncertainty in loadings, which is essential if these results are to provide a benchmark for future studies. The uncertainty estimates may also be useful in an operational context, and this report provides examples of how uncertainty can be used in monitoring-network design and potentially reducing monitoring costs.

Illinois↗

USGS2020

Trends in nutrient and soil loss in Illinois rivers, 1978–2017

Nutrient and soil loss, defined herein as the loss of nutrients or soil to streams and other downstream receiving waters, affect watersheds around the globe. Although governments make large investments mitigating nutrient and soil loss through watershed management efforts, the efficacy of these efforts is often difficult to assess, in part because streamflow variability obscures the effects. This study investigates the effects of watershed management on nutrient and soil losses in the State of Illinois during two periods: 1978 to 2017, and 2008 to 2017. The former period provides an important test case for assessing the efficacy of major Federal programs like the Clean Water Act and the Conservation Reserve Program at mitigating nutrient and soil loss, whereas the latter spans the years after these policies were well established, thereby providing an assessment of whether these programs have kept pace with ongoing trends in climate and watershed management. The effect of interannual streamflow variability on long-term nutrient and soil loss trends was removed using an extension of the Weighted Regressions on Time, Discharge, and Season methodology, called generalized flow normalization. This process also partitions trends into components attributable to long-term changes in streamflow and watershed management. The Weighted Regressions on Time, Discharge, and Season trend analysis indicated significant, widespread trends in nutrient and soil loss in Illinois since 1978. From 1978 to 2017, improvements in watershed management reduced nitrogen and soil loss from watersheds within Illinois, but this effect was partially or entirely negated by increasing losses due to changing streamflow. During the same period, phosphorus loss also increased owing to a combination of inadequate management efforts and changing streamflow. During 2008–17, however, nutrient and soil losses have all accelerated, threatening to undo previous reductions if the current trends continue.

Illinois↗

USGS2015

Southern Salish Sea Habitat Map Series: Admiralty Inlet

In 2010 the Environmental Protection Agency, Region 10 initiated the Puget Sound Scientific Studies and Technical Investigations Assistance Program, designed to support research in support of implementing the Puget Sound Action Agenda. The Action Agenda was created in response to Puget Sound having been designated as one of 28 estuaries of national significance under section 320 of the U.S. Clean Water Act, and its overall goal is to restore the Puget Sound Estuary's environment by 2020. The Southern Salish Sea Mapping Project was funded by the Assistance Program request for proposals process, which also supports a large number of coastal-zone- and ocean-management issues. The issues include the recommendations of the Marine Protected Areas Work Group to the Washington State Legislature (Van Cleve and others, 2009), which endorses a Puget Sound and coast-wide marine conservation needs assessment, gap analysis of existing Marine Protected Areas (MPA) and recommendations for action. This publication is the first of four U.S. Geological Survey Scientific Investigation Maps that make up the Southern Salish Sea Mapping Project. The remaining three map blocks to be published in the future, located south of Admiralty Inlet, are shown in figure 1. Puget Sound is a deep, fjord-type estuary covering an area of 2,330 km 2 in the Pacific Northwest region of the United States (fig. 1). It is connected to the ocean by the Strait of Juan de Fuca, a turbulent passage approximately 160 km in length and 22 km wide at its west end, expanding to over 40 km wide at its east end (Thomson, 1994). During the Pleistocene, the area was occupied several times by lobes of continental ice, resulting in a complex basin-fill of glacial and interglacial deposits that are locally as thick as 1100 m (Johnson and others, 2001). The last glaciation, called the Fraser glaciation, began after 28,800±740 14 C yr B.P. when ice started a slow expansion (Clague, 1981). At peak advance the westward Juan de Fuca lobe reached the edge of the continental shelf through the Juan de Fuca Strait shortly before 14,460±200 14 C yr B.P. (Herzer and Bornhold, 1982). The southward Puget lobe advanced to its terminal position in Puget Sound by around 14,150 14 C yr B.P. (Porter and Swanson, 1998). Ice retreated from its maximum to northern Whidbey Island by 13,650±350 14 C yr B.P. (Dethier and others, 1995). Retreating glaciers resulted in a thick sequence of ice-contact, glacial-marine sediment, and early post-glacial sediments (Linden and Schurrer, 1988). These deposits have experienced the effects of a marine transgression followed by regression, resulting in a sea-level several tens of meters lower than the present day (Linden and Schurrer, 1988). A second transgression brought sea level to about the present level by around 5,470±120 14 C yr B.P. (Clague and others, 1982) establishing the present oceanographic and geologic environment Puget Sound is separated into four interconnected basins; Whidbey, Central (Main), Hood Canal, and South (Thomson, 1994). The Whidbey, Central, and Hood Canal basins are the three main branches of the Puget Sound estuary and are separated from the Strait of Juan de Fuca by a double sill at Admiralty Inlet. The Admiralty Inlet map area includes the Inlet and a portion of the Whidbey Basin (fig. 1). The shallower South Basin is separated by a sill at Tacoma Narrows and is highly branched with numerous finger inlets. Flow within Puget Sound is dominated by tidal currents of as much as 1 m/s at Admiralty Inlet, reducing to approximately 0.5 m/s in the Central Basin (Lavelle and others, 1988). The lack of silt and clay-sized sediments in the Admiralty Inlet map area is likely a result of the strong currents (see Ground-Truth Studies for the Admiralty Inlet Map Area, sheet 3). The subtidal component of flow reaches approximately 0.1 m/s and is driven by density gradients arising from the contrast in salty ocean water at the entrance and freshwater inputs from stream flow (Lavelle and others, 1988). The total freshwater input to Puget Sound is approximately 3.4 x 10 6 m 3 /day, primarily from the Skagit River (Cannon, 1983). The subtidal circulation mostly consists of a two-layered flow in the basins with fresher water exiting at the surface and saltier water entering at depth (Ebbesmeyer and Cannon, 2001). In general, surface waters flow north and deeper waters flow south; variations arise from wind effects that can drive a surface current in the same direction as the wind, and a baroclinic response in the lower layer to about 100-m depth (Matsuura and Cannon, 1997). Oceanographic properties are influenced by temporal forcing parameters such as reduced stream flow during the 2000-01 drought that increased surface salinity and decreased differences between surface and bottom waters (Newton and others, 2003). On offshore seismic-reflection profiles, Pleistocene strata (excluding latest Pleistocene glacial and post-glacial deposits) form a distinct seismic unit, bounded below by pre-Tertiary or Tertiary basement and above by typically flat-lying latest Pleistocene to Holocene deposits that fill in erosional or depositional relief (Johnson and others, 2001). Cores from central Puget Sound have accumulation rates that range from 85 to 1200 mg/cm 2 /yr, or 0.12 to 2.4 cm/yr; the highest accumulation rates are near the southern end of central Puget Sound (Carpenter and others, 1985). Carpenter and others (1985) un-weighted arithmetic mean of accumulation rates for central Puget Sound deeper stations is 480±340 (± one standard deviation) mg/cm 2 /yr. Lavelle and others (1985) also found rates as high as 1200 mg/cm 2 /yr over the past approximately 70 years in cores in the Central Basin off of and north and south of Elliott Bay. Puget Sound basin rates are comparable to rates in midshelf silt deposits on the Washington coast north of the Columbia River (Nittrouer and others, 1979). The deep subtidal (in other words, below SCUBA depths) habitats of Puget Sound are relatively poorly known. A few subtidal surveys exist for several habitat types from the 1960s and 1970s (reviewed in Dethier, 1990), using grab and box core data. The Dethier (1990) review divides habitat up into Coast and Marine Ecological Classification Standard (CMECS) substrate, water column energy, and depth zones but does not attempt to map these habitats, rather it is an inventory of habitats found in the area and the flora and fauna associated with each habitat. The approach of the Southern Salish Sea Mapping project is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, and bottom-sediment sampling data. This approach is based in part on methods presented and data collection and product needs identified at the Washington State Seafloor Mapping Workshop (Washington State Seafloor Mapping Workshop Steering Committee, 2008), attended by coastal and marine managers and scientists. The map products display seafloor geomorphology and substrate, and identify potential marine benthic habitats. It is emphasized that the more interpretive habitat and geology maps rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. Oceanographic current and wave data is not included in this analysis, however, the accompanying geographic information system (GIS) data set is designed and intended to be combined with oceanographic and biologic data sets assembled by others in the future and some of the GIS data has already been incorporated in the unpublished Nature Conservancy Benthic Habitats of Puget Sound database. This publication includes four map sheets, explanatory text, and a descriptive pamphlet. Each map sheet is published as a portable document format (PDF) file. ESRI ArcGIS compatible geotiffs (for example, bathymetry) and shapefiles (for example video observation points) will be available for download in the data catalog associated with this publication (Cochrane, 2015). An ArcGIS Project File with the symbology used to generate the map sheets is also provided. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html .

Washington↗

USGS2013

Marine benthic habitat mapping of the West Arm, Glacier Bay National Park and Preserve, Alaska

Seafloor geology and potential benthic habitats were mapped in West Arm, Glacier Bay National Park and Preserve, Alaska, using multibeam sonar, groundtruthed observations, and geological interpretations. The West Arm of Glacier Bay is a recently deglaciated fjord system under the influence of glacial and paraglacial marine processes. High glacially derived sediment and meltwater fluxes, slope instabilities, and variable bathymetry result in a highly dynamic estuarine environment and benthic ecosystem. We characterize the fjord seafloor and potential benthic habitats using the recently developed Coastal and Marine Ecological Classification Standard (CMECS) by the National Oceanic and Atmospheric Administration (NOAA) and NatureServe. Due to the high flux of glacially sourced fines, mud is the dominant substrate within the West Arm. Water-column characteristics are addressed using a combination of CTD and circulation model results. We also present sediment accumulation data derived from differential bathymetry. These data show the West Arm is divided into two contrasting environments: a dynamic upper fjord and a relatively static lower fjord. The results of these analyses serve as a test of the CMECS classification scheme and as a baseline for ongoing and future mapping efforts and correlations between seafloor substrate, benthic habitats, and glacimarine processes.

Alaska↗