ABSTRACT:

Diagnose Aquatic Sensor Data for Temperature and Water Quality Events

## Overview
This project is designed to diagnose and flag events in aquatic sensor data based on various conditions and thresholds. It processes raw data from aquatic sites and applies thresholds and logical conditions to identify different types of anomalies. The primary focus is to flag events that may indicate sensor anomalies, environmental conditions (e.g., frozen water), or technician site visits.

### Key Features
1. Event Detection: Detects and flags various event types, such as MNT (maintenance), LWT (low water table), ICE (frozen water), SLM (sensor logger malfunction), PF (power failure), and VIN (visual inspection).
2. Data Quality Control: Uses thresholds to validate sensor readings, ensuring accurate representation of water conditions.
3. Automated Labelling: Automatically labels events using a set of predefined indicators for anomaly detection.

Workflow of the model:
https://ibb.co/8BDFjsv

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 package includes data, metadata, and script which enables us to provide comprehensive retrieval data from the USGS gages using the Jupyter notebook server. This code can retrieve the Discharge variable at the LITTLE BEAR RIVER AT PARADISE, UT site. Also, the results are visualized by different Python packages. More detailed information about the site name/code, parameter code, uSGS webpage, and temporal range of retrieved data is mentioned in the 'read_me' file.