ABSTRACT:

Underwood et al. (2023) have recently introduced the tandem evolutionary algorithm (TEVA) of Hanley et al. (2020) to the water resources and ecology domains, and applied it to identify features (catchment-scale attributes) and feature interactions important in determining patterns in Dissolved Organic Carbon across the continental US. TEVA has particular advantages for feature selection in large, multivariate observational data sets of complex systems like riverscapes or ecosystems, and has been shown to outperform logistic regression or Random Forest for identifying feature interactions and equifinality (Hanley et al., 2020; Anderson et al., 2020). TEVA finds interactions between multiple variables that may result from either additive processes or feature interactions, and not only extracts features significantly associated with a given outcome class(es), but also identifies the specific value ranges associated with those features (Underwood et al., 2023; Hanley, et al., 2020). This algorithm is also robust to issues of mixed data types (continuous, categorical), missing data, censored data, skewed distributions, and unbalanced target classes or clusters (Hanley et al., 2020).

When presented with n observations of p features across a study domain and a target of one or more classes or outcomes, the algorithm identifies and archives two types of clauses below a given fitness threshold. In the first pass, TEVA identifies Conjunctive Clauses (CCs) - a combination of variables that may or may not be correlated and somehow interact to produce an outcome. For example, an Extreme Flood may result from steep slopes + shallow soils + intense rainfall. A second pass of TEVA identifies Disjunctive Clauses (DCs) - a sequence of CCs that are linked with a logical “OR” statement. For example, an Extreme Flood may results from (steep slopes + shallow soils + intense rainfall) OR (high antecedent soil moisture + rainfall) OR (thick snow pack + high temperatures). DCs are multi-order, while the CCs comprising a DC can themselves range from first-order to multi-order (Underwood et al., 2023).

In this workshop, we illustrate the functionality of TEVA using a dataset of 91 observations from forested catchments across the CONUS of 54 catchment attributes inferred to have importance to DOC dynamics. Combinations of these catchment attributes were identified in CCs and DCs with high probability to be linked to an outcome class of High or Low mean DOC concentration. Target classes were assigned using Jenks natural breaks for 91 catchments with sufficient (≥3) observations of DOC in stream water to calculate a mean value. Originally, computation of TEVA was performed in the MATLAB programming language; the codebase has now been transferred to the open-source coding language Python, and is accessed through CUAHSI JupyterHub.

ABSTRACT:

Underwood et al. (2023) have recently introduced the tandem evolutionary algorithm (TEVA) of Hanley et al. (2020) to the water resources and ecology domains, and applied it to identify features (catchment-scale attributes) and feature interactions important in determining patterns in Dissolved Organic Carbon across the continental US. TEVA has particular advantages for feature selection in large, multivariate observational data sets of complex systems like riverscapes or ecosystems, and has been shown to outperform logistic regression or random forest for identifying feature interactions and equifinality (Hanley et al., 2020; Anderson et al., 2020). TEVA finds interactions between multiple variables that may result from either additive processes or feature interactions, and not only extracts features significantly associated with a given outcome classe(s), but also identifies the specific value ranges associated with those features (Underwood et al., 2023; Hanley, et al., 2020). This algorithm is also robust to issues of mixed data types (continuous, categorical), missing data, censored data, skewed distributions, and unbalanced target classes or clusters (Hanley et al., 2020).

When presented with n observations of p features across a study domain and a target of one or more classes or outcomes, the algorithm identifies and archives two types of clauses below a given fitness threshold. In the first pass, TEVA identifies Conjunctive Clauses (CCs) - a combination of variables that may or may not be correlated and somehow interact to produce an outcome. For example, an Extreme Flood may result from steep slopes + shallow soils + intense rainfall. A second pass of TEVA identifies Disjunctive Clauses (DCs) - a sequence of CCs that are linked with a logical “OR” statement. For example, an Extreme Flood may results from (steep slopes + shallow soils + intense rainfall) OR (high antecedent soil moisture + rainfall) OR (thick snow pack + high temperatures). DCs are multi-order, while the CCs comprising a DC can themselves range from first-order to multi-order (Underwood et al., 2023).

In this workshop, we illustrate the application of TEVA to 91 observations from forested catchments across the CONUS of 54 catchment attributes inferred to have importance to DOC dynamics. Combinations of these catchment attributes were identified in CCs and DCs with high probability to be linked to an outcome class of High or Low mean DOC concentration. Target classes were assigned using Jenks natural breaks for 91 catchments with sufficient (≥3) observations of DOC in stream water to calculate a mean value. Originally, computation of TEVA was performed in the Matlab programming language; the codebase has now been transferred to the open-source coding language Python, and is accessed through CUAHSI’s JupyterHub.

ABSTRACT:

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ABSTRACT:

This resource contains instructional materials from a virtual training held on August 12, 2025, for primarily Research Experiences for Undergraduates (REU) students at the University of Northern Illinois. The session introduced participants to the concept and value of scientific data repositories, with a focus on HydroShare. Training topics included creating a HydroShare account, exploring and filtering datasets through the Discover page, uploading and describing new resources, and locating documentation and support. The materials are designed to help new users navigate HydroShare effectively and understand how to share and publish their own data.

ABSTRACT:

This HydroShare resource contains an educational Jupyter Notebook demoing how to access, process and visualize water data from the United States Geological Survey (USGS). The notebook leverages the dataRetrieval R package to programmatically discover monitoring sites based on their proximity to a user-defined landmark and visualize time series water data.

ABSTRACT:

The CUAHSI Water Learning Hub (https://water-content-portal.cuahsi.io/) is an open learning platform that brings together educational materials for water research. The portal includes tutorials, reusable workflows, and instructional content designed to support students, researchers, and practitioners working with water data.

This platform organizes diverse educational materials into five distinct categories for water researchers, bringing them together in a single, accessible space. Materials are designed to be reusable, transparent, and adaptable for teaching and research applications.

This HydroShare resource serves as a collection that references the full portal and links to individual collections and pages that are published as separate, citable resources.

ABSTRACT:

The Hydroinformatics Book (https://www.hydroinformaticsbook.com) is a collection of tutorials and instructional materials focused on working with water data using computational tools. Topics include data access, visualization, analysis, and reusable workflows in R.

The book is designed for learners with a range of experience levels and emphasizes hands-on examples that can be adapted for research and teaching. All materials are openly available and developed using a version-controlled workflow.

This HydroShare resource serves as a collection for the Hydroinformatics book and links to individual chapters that are published as separate, citable resources.

ABSTRACT:

This resource represents Chapter 1 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/plotting-demo-complete) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 2 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/plotting-demo-complete) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 3 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/activity-intro-skills/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 4 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/intro-stats-completed/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 5 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/intro-stats-activity/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 6 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/get-format-plot-hydrodata-complete/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 7 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/activity-joins-pivots-dataretrieval/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 8 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/summative-activity/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 9 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/flow-duration-curves/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 9 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/flow-duration-curves/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 11 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/flood-frequency/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 12 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/intro-geospatial-r/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 13 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/geospatial-raster-hydro/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 14 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/summative-2-student/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 15 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/watershed-delineation-and-analysis/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 16 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/modeling-getting-started-hbv/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This resource represents Chapter 17 from the Hydroinformatics book (https://water-content-portal.cuahsi.io/calibrate-hbv/) and introduces concepts and workflows for working with water data in R.

The material includes step-by-step examples, code, and explanations designed to support learning and reuse. Content is openly available and can be adapted for teaching or research purposes.

This HydroShare resource provides a citable version of the chapter and includes a snapshot of the source repository.

ABSTRACT:

This notebooks explores methods for working with large cloud data stores using tools such as xarray, dask, and geopandas. This exploration is uses the Analysis of Record for Collaboration (AORC) meteorological dataset that is used by the NOAA National Water Model. This notebook provides examples for how to access, slice, and visualize a large cloud-hosted dataset as well as an approach for aligning these data with watershed vector boundaries.

The data used in this notebook can be found at https://registry.opendata.aws/nwm-archive/.

ABSTRACT:

This HydroShare resource contains survey data collected by students in the Data Warriors program (https://thedatawarriors.com/) at Henninger High School in Syracuse, NY. The survey was created in response to an especially snowy 2025-26 winter and concerns that some students were having difficulty getting to school on time due to streets near their homes not being cleared quickly after snowstorms.

Questions asked students how often they were late because of snow, how they usually travel to school, what time they leave home, whether the street near their home is usually plowed on snowy days, and what street they live on. The survey was motivated in part by the idea that students living on smaller residential streets and in different areas of Syracuse may face greater challenges getting to school than students who live on roads that are plowed more quickly.

This data may be useful for understanding how differences in municipal snow removal across Syracuse neighborhoods shape students' ability to get to school on time and may help inform future conversations about winter transportation and equitable access to education.