ABSTRACT:

This study presents a comprehensive comparison of gridded datasets for the Great Salt Lake (GSL) basin, focusing on precipitation and temperature as the main inputs for hydrological balances. The evaluated gridded datasets include PRISM, DAYMET, GRIDMET, NLDAS-2, and CONUS404, with in-situ data used for assessing alignment and accuracy. Key metrics such as Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) were employed to evaluate gridded dataset performance. Spatial and temporal accuracy analyses were conducted across different GSL basin regions to understand variations in accuracy. DAYMET emerged as the leading dataset for precipitation across most metrics, demonstrating consistent performance. For temperature, GRIDMET and PRISM ranked higher, indicating better representation of temperature patterns in the GSL basin. Spatial analysis revealed variability in accuracy for both temperature and precipitation data, emphasizing the importance of selecting suitable datasets for different regions to enhance overall accuracy. The insights from this study can inform environmental forecasting and water resource management in the GSL basin, assisting researchers and decision-makers in choosing reliable gridded datasets for hydrological studies.

ABSTRACT:

This resource provides the complete reproducibility package for the study Developing Testing Datasets for Benchmarking Automated Quality Control Algorithm Performance with Aquatic Sensor Data. It contains a labeled benchmark dataset derived from 2020–2024 observations at five Logan River Observatory monitoring sites in northern Utah, USA. The resource includes raw aquatic and climate sensor data, manually quality-controlled data, field notes, configuration files, Python code, and the resulting labeled time-series and anomaly-event datasets.
The workflow identifies technician-QC-derived anomaly events by comparing raw and manually corrected observations, then assigns point-level and event-level labels, anomaly patterns, inferred causes, and event features. The benchmark covers water temperature, pH, dissolved oxygen, water stage, and specific conductance. It is intended to support transparent, reproducible evaluation and comparison of automated quality-control and anomaly-detection methods for aquatic sensor time series, and to provide a reusable workflow that can be extended to other monitoring networks and hydrologic settings.

ABSTRACT:

Result of the Casualty-ML course at The University of Alabama.

For reproducing the results:
1. Run the MI notebook
2. Run the LSTM notebook

ABSTRACT:

Operational flood early warning requires continuous ingestion of forcing data, execution of hydrodynamic models, and translation of outputs into actionable alerts. These steps are often manual or semi-automated, limiting operational use of high-resolution two-dimensional flood models. This paper presents HECinBOX, an open-source, containerized framework that transforms existing HEC-RAS models into continuously operating flood early-warning systems. HECinBOX integrates HEC-RAS simulations with a configurable warning agent, automates retrieval and injection of gauge/forecast data into model boundary conditions, and enables scheduled flood forecasting, mapping, and alert generation. Deterministic modules handle model scanning, data ingestion, boundary condition orchestration, headless HEC-RAS execution, and result extraction, while a stateful agent evaluates user-defined thresholds, tracks prior alerts, and issues alerts. Tested on several ready-to-run HEC-RAS models, including Brays Bayou in Houston, Texas, HECinBOX demonstrates applicability across model configurations and provides a generalizable path from static flood studies to operational, self-updating early-warning systems.

ABSTRACT:

This resource contains CyberWater2 training workflows and supporting results for Activities 1–3. The materials demonstrate retrieval and visualization of USGS observations, VIC hydrologic modeling and routing, and integration of a One-Layer Water Balance Model using the Generic Model Agent Toolkit. The VisTrails workflow files can be opened and executed in CyberWater with the required software, model files, credentials, and input data.

ABSTRACT:

This resource presents a conceptual CyberWater workflow for simulating and mapping compound flooding in Mobile Bay, Alabama. The proposed framework integrates precipitation, watershed runoff, river discharge, tides, storm surge, terrain, and land-surface data with hydrologic and hydrodynamic modeling components. The included README identifies the required inputs, existing CyberWater capabilities, missing development needs, expected outputs, evaluation methods, and major uncertainties. The conceptual workflow diagram illustrates how these components could be connected to produce flood-depth and inundation-extent maps for coastal risk assessment.