Browse more than 5,500 book chapters authored by our scientists over the past 100+ year history of the USGS and refine search by topic, location, year, and advanced search.
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Machine learning for understanding inland water quantity, quality, and ecology
This chapter provides an overview of machine learning models and their applications to the science of inland waters. Such models serve a wide range of purposes for science and management: predicting water quality, quantity, or ecological dynamics across space, time, or hypothetical scenarios; vetting and distilling raw data for further modeling or analysis; generating and exploring hypotheses; est
Landslides triggered by the 2002 M 7.9 Denali Fault earthquake, Alaska, USA
The 2002 M 7.9 Denali earthquake in Alaska, USA, was the largest inland earthquake in North America in nearly 150 years. The earthquake involved oblique thrusting but mostly strike-slip motion, and faults ruptured the ground surface over 330 km. Fault rupture occurred in a rugged, mountainous, subarctic environment with extensive permafrost and variable glaciation, geology, and groundwater presenc
Osmoregulation and acid-base balance.
Maintaining relatively constant levels of internal cellular ions is critical to the normal function of all animals. For many organisms this is achieved primarily by regulating the ion and acid-base composition of the blood within narrow limits. This understanding of the importance of “le milieu interior,” first espoused by Claude Bernard in the mid-1800s and later described as “homeostasis” by Wal
Horseshoe crab
No abstract available.
The Colorado River – The science-policy interface
No abstract available.
Invasive species control and management: The sea lamprey story
Control of invasive species is a critical component of conservation biology given the catastrophic damage that they can cause to the ecosystems they invade. This is particularly evident with sea lamprey (Petromyzon marinus) in the Laurentian Great Lakes. Native to the Atlantic Ocean, the sea lamprey's ability to osmoregulate in fresh water, its wide thermal tolerance, generalist diet, and high fec
Tectonics, fault zones, and topography in the Alaska-Canada Cordillera with a focus on the Alaska Range and Denali fault zone
Synergistic interactions between geologic structures and topography have long been recognized to reflect numerous Earth processes and rock properties over time. It was not until the advent of plate tectonics in the midtwentieth century that researchers began to view the nature of the northern Cordillera orogen as a quilt of foreign pieces of crust or “suspect terranes”. The Alaska Range shows comp
Biological assessments of aquatic ecosystems
The aim of biological assessments (or bioassessments) is to provide decision makers and managers the scientific information and tools needed to protect and restore aquatic life. Biological assessments typically include several critical elements, including development of ecological indicators, indices of ecological status, benchmarks by which to gauge impairment, ways to identify the stressors caus
Physics-guided architecture (PGA) of LSTM models for uncertainty quantification in lake temperature modeling
This chapter focuses on meeting the need to produce neural network outputs that are physically consistent and also express uncertainties, a rare combination to date. It explains the effectiveness of physics-guided architecture - long-short-term-memory (PGA-LSTM) in achieving better generalizability and physical consistency over data collected from Lake Mendota in Wisconsin and Falling Creek Reserv
Heat budget of lakes
This article gives an overview of the heat fluxes between lakes and their environment. The heat budget of most lakes is dominated by heat fluxes at the lake surface, especially shortwave radiation, incoming and outgoing longwave radiation, and the latent heat flux. The seasonality of these fluxes is the most important driver for seasonal mixing processes in lakes. Changes in heat fluxes and the re
Physics-guided neural networks (PGNN): An application in lake temperature modeling
This chapter introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. It explains termed physics-guided neural networks (PGNN), leverages the output of physics-based model simulations along with observational features in a hybrid modeling setup to generate predictions using a neural network architecture. Data science ha
Physics-guided recurrent neural networks for predicting lake water temperature
This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. M