Reed Group Research at the AGU 2024 Conference
Published:
Our group is currently preparing to head to Washington D.C. for the annual American Geophysical Union (AGU) conference.
Posts by Tags
AGU
Bayesian
Introduction to Bayesian Regression using PyMC
Published:
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.
Bayesian Methods
Bayesian Additive Regression Trees for Probabilistic Scenario Discovery
Published:
In this post, I provide an argument for using Bayesian Additive Regression Trees (BART) in scenario discovery contexts, highlighting the strengths of the BART model compared to other models used in the literature. I give a very quick introduction to the BART model formulation. Finally, I demonstrate the benefit of using BART for scenario discovery (AKA factor mapping) compared to deterministic Gradient Boosted Tree methods using the Shallow Lake Problem.
Climate
Rodionov Regime Shift Detection Algorithm and the “Epic Pluvial” in the Delaware River Basin
Published:
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?
Conferences
Reed Group Research at the AGU 2024 Conference
Published:
Our group is currently preparing to head to Washington D.C. for the annual American Geophysical Union (AGU) conference.
WaterSciCon24: Themes, Highlights, and Links
Published:
Last week I had the chance to travel to St. Paul, Minnesota, for the WaterSciCon conference hosted by AGU and CUAHSI. In this post, I give some of my own reflections on the core themes from the conference and share links and resources that were highlighted during the sessions.
DRB
Rodionov Regime Shift Detection Algorithm and the “Epic Pluvial” in the Delaware River Basin
Published:
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?
Data Visualization
Sankey Diagrams for USGS Gauge Data in Python(?)
Published:
This post was inspired by the Sankey diagram in Figure 1 of this pre-print led by Dave Gold: “Exploring the Spatially Compounding Multi-sectoral Drought Vulnerabilities in Colorado’s West Slope River Basins” (Gold, Reed & Gupta, In Review) which features a Sankey diagram of flow contributions to Lake Powell. I like the figure, and thought I’d make an effort to produce similar diagrams using USGS gauge data.
Daymet
Daymet data retrieval in Python using the Planetary Computer
Published:
Motivation:
Delaware River Basin
Pywr-DRB Part 2: Running Ensemble Simulations with the DRB Reconstruction Ensemble
Published:
Motivation
Direct Policy Search
Introduction to Radial Basis Functions for Adaptive Control Problems
Published:
Radial basis functions (RBFs) have been used for adaptive system control in multiple water resource systems publications. They show up regularly in blog posts, such as here, and here, and others. However, they have never gotten their own detailed post.
Ensemble
The Thomas-Fiering Model for Synthetic Streamflow Generation - Python Implementation
Published:
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.
Folium
Interactive Geo-Spatial Maps with Folium for Python
Published:
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.
HPC
Pywr-DRB Part 2: Running Ensemble Simulations with the DRB Reconstruction Ensemble
Published:
Motivation
HyRiver
Hydrologic Data Retrieval with HyRiver for Python
Published:
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.
Hydrologic Modeling
Scaling Gauged Inflow Data Using Hydrologic Model Outputs for Better Reservoir Simulations
Published:
Motivation
Hydrology
The Thomas-Fiering Model for Synthetic Streamflow Generation - Python Implementation
Published:
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.
Overview of the National Hydrologic Model
Published:
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.
LLM
Custom Model Context Protocols to Improve LLM Supported Code Development
Published:
Motivation:
LaTeX
Markdown-Based Scientific and Computational Note Taking with Obsidian
Published:
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.
Linear Programming
An Introduction to Linear Programming for Reservoir Operations (Part 2): Implementation with Pyomo
Published:
Introduction
Previously, in Part 1 I used a very simple reservoir operations scenario to demonstrate some linear programming (LP) concepts.
An Introduction to Linear Programming for Reservoir Operations (Part 1)
Published:
Originally published on the WaterProgramming blog. The formulation below reflects the revised version of the post, which optimizes both releases and storage over multiple timesteps. See Part 2 for the Python implementation using Pyomo.
MCMC
Introduction to Bayesian Regression using PyMC
Published:
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.
MCP
Custom Model Context Protocols to Improve LLM Supported Code Development
Published:
Motivation:
MOEAs
Fisheries Training Part 1 Harvest Optimization and MOEA Diagnostics
Published:
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.
Maps
Interactive Geo-Spatial Maps with Folium for Python
Published:
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.
Markdown
Markdown-Based Scientific and Computational Note Taking with Obsidian
Published:
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.
NHM
Overview of the National Hydrologic Model
Published:
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.
National Water Model
Scaling Gauged Inflow Data Using Hydrologic Model Outputs for Better Reservoir Simulations
Published:
Motivation
Obsidian
Markdown-Based Scientific and Computational Note Taking with Obsidian
Published:
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.
Open Science
WaterSciCon24: Themes, Highlights, and Links
Published:
Last week I had the chance to travel to St. Paul, Minnesota, for the WaterSciCon conference hosted by AGU and CUAHSI. In this post, I give some of my own reflections on the core themes from the conference and share links and resources that were highlighted during the sessions.
Optimization
Introduction to Radial Basis Functions for Adaptive Control Problems
Published:
Radial basis functions (RBFs) have been used for adaptive system control in multiple water resource systems publications. They show up regularly in blog posts, such as here, and here, and others. However, they have never gotten their own detailed post.
An Introduction to Linear Programming for Reservoir Operations (Part 2): Implementation with Pyomo
Published:
Introduction
Previously, in Part 1 I used a very simple reservoir operations scenario to demonstrate some linear programming (LP) concepts.
An Introduction to Linear Programming for Reservoir Operations (Part 1)
Published:
Originally published on the WaterProgramming blog. The formulation below reflects the revised version of the post, which optimizes both releases and storage over multiple timesteps. See Part 2 for the Python implementation using Pyomo.
PUB
QPPQ Method for Streamflow Predictions in Ungauged Basins
Published:
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).
Plotly
Sankey Diagrams for USGS Gauge Data in Python(?)
Published:
This post was inspired by the Sankey diagram in Figure 1 of this pre-print led by Dave Gold: “Exploring the Spatially Compounding Multi-sectoral Drought Vulnerabilities in Colorado’s West Slope River Basins” (Gold, Reed & Gupta, In Review) which features a Sankey diagram of flow contributions to Lake Powell. I like the figure, and thought I’d make an effort to produce similar diagrams using USGS gauge data.
PyMC
Bayesian Additive Regression Trees for Probabilistic Scenario Discovery
Published:
In this post, I provide an argument for using Bayesian Additive Regression Trees (BART) in scenario discovery contexts, highlighting the strengths of the BART model compared to other models used in the literature. I give a very quick introduction to the BART model formulation. Finally, I demonstrate the benefit of using BART for scenario discovery (AKA factor mapping) compared to deterministic Gradient Boosted Tree methods using the Shallow Lake Problem.
Introduction to Bayesian Regression using PyMC
Published:
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.
Pyomo
An Introduction to Linear Programming for Reservoir Operations (Part 2): Implementation with Pyomo
Published:
Introduction
Previously, in Part 1 I used a very simple reservoir operations scenario to demonstrate some linear programming (LP) concepts.
Python
Pywr-DRB Part 2: Running Ensemble Simulations with the DRB Reconstruction Ensemble
Published:
Motivation
Daymet data retrieval in Python using the Planetary Computer
Published:
Motivation:
Bayesian Additive Regression Trees for Probabilistic Scenario Discovery
Published:
In this post, I provide an argument for using Bayesian Additive Regression Trees (BART) in scenario discovery contexts, highlighting the strengths of the BART model compared to other models used in the literature. I give a very quick introduction to the BART model formulation. Finally, I demonstrate the benefit of using BART for scenario discovery (AKA factor mapping) compared to deterministic Gradient Boosted Tree methods using the Shallow Lake Problem.
Sankey Diagrams for USGS Gauge Data in Python(?)
Published:
This post was inspired by the Sankey diagram in Figure 1 of this pre-print led by Dave Gold: “Exploring the Spatially Compounding Multi-sectoral Drought Vulnerabilities in Colorado’s West Slope River Basins” (Gold, Reed & Gupta, In Review) which features a Sankey diagram of flow contributions to Lake Powell. I like the figure, and thought I’d make an effort to produce similar diagrams using USGS gauge data.
Introduction to Bayesian Regression using PyMC
Published:
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.
Rodionov Regime Shift Detection Algorithm and the “Epic Pluvial” in the Delaware River Basin
Published:
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?
Interactive Geo-Spatial Maps with Folium for Python
Published:
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.
Structuring a Python Project - Recommendations and a Template Example
Published:
The start of a new year is a good (albeit, relatively arbitrary) time to reassess aspects of your workflow.
QPPQ Method for Streamflow Predictions in Ungauged Basins
Published:
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).
Hydrologic Data Retrieval with HyRiver for Python
Published:
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.
Fisheries Training Part 1 Harvest Optimization and MOEA Diagnostics
Published:
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.
PywrDRB
Custom Model Context Protocols to Improve LLM Supported Code Development
Published:
Motivation:
Pywr-DRB Part 2: Running Ensemble Simulations with the DRB Reconstruction Ensemble
Published:
Motivation
Scaling Gauged Inflow Data Using Hydrologic Model Outputs for Better Reservoir Simulations
Published:
Motivation
QPPQ
QPPQ Method for Streamflow Predictions in Ungauged Basins
Published:
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).
Radial Basis Functions
Introduction to Radial Basis Functions for Adaptive Control Problems
Published:
Radial basis functions (RBFs) have been used for adaptive system control in multiple water resource systems publications. They show up regularly in blog posts, such as here, and here, and others. However, they have never gotten their own detailed post.
Reed Group
Reed Group Research at the AGU 2024 Conference
Published:
Our group is currently preparing to head to Washington D.C. for the annual American Geophysical Union (AGU) conference.
12 Years of WaterProgramming: A Retrospective on 500+ Blog Posts
Published:
Just over 12 years ago, on January 9th 2012, the first WaterProgramming post was published. It was written by Joe Kasprzyk who is now an Associate Professor at CU Boulder, but at the time was a graduate student in the Reed Research Group. The post reads, in it’s entirety:
Welcome!
“This blog shares tips for writing programs and running jobs associated with using multiobjective evolutionary algorithms (MOEAs) for water resources engineering. It will be informal, with posts on a number of topics by a number of folks.”
Regime
Rodionov Regime Shift Detection Algorithm and the “Epic Pluvial” in the Delaware River Basin
Published:
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?
Regression
Introduction to Bayesian Regression using PyMC
Published:
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.
Reservoir Operations
Scaling Gauged Inflow Data Using Hydrologic Model Outputs for Better Reservoir Simulations
Published:
Motivation
An Introduction to Linear Programming for Reservoir Operations (Part 2): Implementation with Pyomo
Published:
Introduction
Previously, in Part 1 I used a very simple reservoir operations scenario to demonstrate some linear programming (LP) concepts.
An Introduction to Linear Programming for Reservoir Operations (Part 1)
Published:
Originally published on the WaterProgramming blog. The formulation below reflects the revised version of the post, which optimizes both releases and storage over multiple timesteps. See Part 2 for the Python implementation using Pyomo.
Scenario Discovery
Bayesian Additive Regression Trees for Probabilistic Scenario Discovery
Published:
In this post, I provide an argument for using Bayesian Additive Regression Trees (BART) in scenario discovery contexts, highlighting the strengths of the BART model compared to other models used in the literature. I give a very quick introduction to the BART model formulation. Finally, I demonstrate the benefit of using BART for scenario discovery (AKA factor mapping) compared to deterministic Gradient Boosted Tree methods using the Shallow Lake Problem.
Synthetic
The Thomas-Fiering Model for Synthetic Streamflow Generation - Python Implementation
Published:
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.
ThomasFiering
The Thomas-Fiering Model for Synthetic Streamflow Generation - Python Implementation
Published:
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.
USGS
WaterSciCon24: Themes, Highlights, and Links
Published:
Last week I had the chance to travel to St. Paul, Minnesota, for the WaterSciCon conference hosted by AGU and CUAHSI. In this post, I give some of my own reflections on the core themes from the conference and share links and resources that were highlighted during the sessions.
Sankey Diagrams for USGS Gauge Data in Python(?)
Published:
This post was inspired by the Sankey diagram in Figure 1 of this pre-print led by Dave Gold: “Exploring the Spatially Compounding Multi-sectoral Drought Vulnerabilities in Colorado’s West Slope River Basins” (Gold, Reed & Gupta, In Review) which features a Sankey diagram of flow contributions to Lake Powell. I like the figure, and thought I’d make an effort to produce similar diagrams using USGS gauge data.
Uncertainty
The Thomas-Fiering Model for Synthetic Streamflow Generation - Python Implementation
Published:
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.
WaterProgramming
12 Years of WaterProgramming: A Retrospective on 500+ Blog Posts
Published:
Just over 12 years ago, on January 9th 2012, the first WaterProgramming post was published. It was written by Joe Kasprzyk who is now an Associate Professor at CU Boulder, but at the time was a graduate student in the Reed Research Group. The post reads, in it’s entirety:
Welcome!
“This blog shares tips for writing programs and running jobs associated with using multiobjective evolutionary algorithms (MOEAs) for water resources engineering. It will be informal, with posts on a number of topics by a number of folks.”
copula
Use of copulas to model joint probability of drought characteristics
Published:
Motivation
drought
Use of copulas to model joint probability of drought characteristics
Published:
Motivation
example
Structuring a Python Project - Recommendations and a Template Example
Published:
The start of a new year is a good (albeit, relatively arbitrary) time to reassess aspects of your workflow.
python
Use of copulas to model joint probability of drought characteristics
Published: