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Fans of this blog will know that uncertainty is often a focus for our group. When approaching uncertainty, Bayesian methods might be of interest since they explicitly provide uncertainty estimates during the modeling process.
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In 1962 a group of economists, engineers and political scientists who were involved in the Harvard Water Program published “Design of Water Resource Systems”. In chapter 12 of the book, Thomas and Fiering present the following statistical model which was one of the first, if not the first, formal application of stochastic modelling for synthetic streamflow generation and water resource systems evaluation.
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Over the past several months, I have been working with data from the US Geological Survey’s (USGS) National Hydrologic Model (NHM), a valuable resource that required some time to become familiar with. The goal of this post is to provide an overview of the NHM, incorporating as many links as possible, with the hope of encouraging others to utilize these resources and serving as a springboard for further investigation.
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In their 2013 publication, Pederson et al. try to answer the question posed by the title: Is an Epic Pluvial Masking the Water Insecurity of the Greater New York City Region?
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Interactive mapping and data visualization provide data scientists and researchers with a unique opportunity to explore and analyze spatial data, and to share their work with stakeholders in a more engaging and accessible way.
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Predicting streamflow at ungauged locations is a classic problem in hydrology which has motivated significant research over the last several decades (Hrachowitz et al., 2013).
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Over the last year, being in the Reed Research Group, I have been exposed to new ideas more rapidly than I can manage. During this period, I was filling my laptop storage with countless .docx, .pdf, .txt, .html files semi-sporadically stored across different cloud and local storages.
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This tutorial highlights the HyRiver software stack for Python, which is a very powerful tool for acquiring large sets of data from various web services.
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This is the second post in a training series, studying decision making under deep uncertainty within the context of a complex harvested predator-prey fishery.
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The start of a new year is a good (albeit, relatively arbitrary) time to reassess aspects of your workflow.