This data release contains the model inputs, outputs, and source code (written in R) for the boosted regression tree (BRT) and artificial neural network (ANN) models developed for four sites in Upper Klamath Lake which were used to simulate daily maximum pH and daily minimum dissolved oxygen (DO) from May 18th to October 4th in 2005-12 and 2015-19 at four sites, and to evaluate variable effects and their importance. Simulations were not developed for 2013 and 2014 due to a large amount of missing meteorological data. The sites included: 1) Williamson River (WMR), which was located in the northern portion of the lake near the mouth of the Williamson River and had a depth between 0.7 and 2.9 meters; 2) Rattlesnake Point (RPT), which was located near the southern portion of the lake and had a depth between 1.9 and 3.4 meters; 3) Mid-North (MDN), which was located in the northwest portion of the lake and a depth between 2.4 and 4.2 meters; 4) Mid-Trench (MDT) , which was located in the trench that runs along the western portion of the lake and had a depth between 13.2 and 15 meters.

We quantified the effects of dam sediment management operations on downstream salmonid spawning habitat during two fall water-level drawdown periods: an experimental drawdown leading to sediment release or a typical slower drawdown intended to minimize release of sediment. The experimental drawdown increased deposited fine sediment and decreased hyporheic dissolved oxygen levels. However, the typical drawdown did not increase fine sediment deposition or decrease hyporheic dissolved oxygen. We quantify the immediate impacts of dam operations using a number of water column and substrate metrics including total suspended sediment concentration, hyporheic dissolved oxygen concentration, and deposited sediment data from sediment infiltration bags.

This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar sites held out. This model archive includes data from 10 sites in the lower Delaware River Basin that were used in the model experiments. Model training target data include dissolved oxygen concentrations downloaded from the National Water Information System (NWIS) (U.S. Geological Survey 2023). Model input data include daily meteorological driver variables derived from gridded surface data (gridMET; Abatzoglou 2013); river and catchment characteristics (Wieczorek et al. 2018); and estimates of daily stream metabolism rates (Appling et al. 2018). The contents of this model archive are organized into files or file directories that have been aggregated into zip files:

This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar sites held out. This model archive includes data from 10 sites in the lower Delaware River Basin that were used in the model experiments. Model training target data include dissolved oxygen concentrations downloaded from the National Water Information System (NWIS) (U.S. Geological Survey 2023). Model input data include daily meteorological driver variables derived from gridded surface data (gridMET; Abatzoglou 2013); river and catchment characteristics (Wieczorek et al. 2018); and estimates of daily stream metabolism rates (Appling et al. 2018). The contents of this model archive are organized into files or file directories that have been aggregated into zip files:

Measurements of Dissolved Oxygen (DO) collected at Klamath River At Keno Bridge. Currently collected twice a year, previously collected quarterly. Access further information for this data set by contacting Bureau of Reclamation, California-Great Basin Region, Environmental Affairs Division (CGB-157). See ResultAttributes for STAFF_GAUGE, SMPL_DEPTH, SMPL_CATEGORY_NAME, METHOD_CODE, RESULT_RL, RESULT_RL-UNIT_STD_NAME, RESULT_MDL, RESULT_MDL-UNIT_STD_NAME, USBR_QA_SUBTYPE_NAME, USBR_QULFR_DESCRIPTION. STAFF_GAUGE is the water height in decimal feet measured by gauge (e.g., 15.2). SMPL_DEPTH is the vertical depth at which sample is collected (e.g., 0 - 15 cm). For water samples: depth below water/air interface. For sediment and soil samples: depth below water/solid or air/solid interface. SMPL_CATEGORY_NAME is the category type of sample (e.g., Composite). METHOD_CODE is the name of method used to obtain result (e.g., EPA 200.8). RESULT_RL is the result reporting limit (accounting for dilution) (e.g., 0.02). RESULT_RL-UNIT_STD_NAME is the unit associated with RESULT_RL (e.g., mg/L). RESULT_MDL is the result method detection limit (e.g., 0.007). RESULT_MDL-UNIT_STD_NAME is the unit associated with RESULT_MDL (e.g., mg/L). USBR_QA_SUBTYPE_NAME is the quality control type of the sample (e.g., USBR_BLANK_SPIKE). USBR_QULFR_DESCRIPTION is the quality assurance description (if any) (e.g., Result may have a high bias.).

This data release contains a boosted regression tree (BRT) model (written in the R programming language), and the input and output data from that model that were used to relate base flow nitrate concentrations in the Chesapeake Bay watershed to catchment characteristics. The input data consists of two types of information: 1) surface water nitrate concentrations collected by the USGS and partnering agencies in the Chesapeake Bay watershed between 1970 and 2013 and 2) potential predictor variables that included nitrogen sources, catchment characteristics, soil and groundwater chemistry, soil drainage and composition, and aquifer geology. The results from the BRT model were used to identify ten significant predictors of base flow nitrate concentrations in streams in the Chesapeake Bay watershed.

This data release contains a boosted regression tree (BRT) model (written in the R programming language), and the input and output data from that model that were used to relate base flow nitrate concentrations in the Chesapeake Bay watershed to catchment characteristics. The input data consists of two types of information: 1) surface water nitrate concentrations collected by the USGS and partnering agencies in the Chesapeake Bay watershed between 1970 and 2013 and 2) potential predictor variables that included nitrogen sources, catchment characteristics, soil and groundwater chemistry, soil drainage and composition, and aquifer geology. The results from the BRT model were used to identify ten significant predictors of base flow nitrate concentrations in streams in the Chesapeake Bay watershed.

Measurements of Chemical Oxygen Demand (COD) collected at Klamath River At Keno Bridge. Currently collected twice a year, previously collected quarterly. Access further information for this data set by contacting Bureau of Reclamation, California-Great Basin Region, Environmental Affairs Division (CGB-157). See ResultAttributes for STAFF_GAUGE, SMPL_DEPTH, SMPL_CATEGORY_NAME, METHOD_CODE, RESULT_RL, RESULT_RL-UNIT_STD_NAME, RESULT_MDL, RESULT_MDL-UNIT_STD_NAME, USBR_QA_SUBTYPE_NAME, USBR_QULFR_DESCRIPTION. STAFF_GAUGE is the water height in decimal feet measured by gauge (e.g., 15.2). SMPL_DEPTH is the vertical depth at which sample is collected (e.g., 0 - 15 cm). For water samples: depth below water/air interface. For sediment and soil samples: depth below water/solid or air/solid interface. SMPL_CATEGORY_NAME is the category type of sample (e.g., Composite). METHOD_CODE is the name of method used to obtain result (e.g., EPA 200.8). RESULT_RL is the result reporting limit (accounting for dilution) (e.g., 0.02). RESULT_RL-UNIT_STD_NAME is the unit associated with RESULT_RL (e.g., mg/L). RESULT_MDL is the result method detection limit (e.g., 0.007). RESULT_MDL-UNIT_STD_NAME is the unit associated with RESULT_MDL (e.g., mg/L). USBR_QA_SUBTYPE_NAME is the quality control type of the sample (e.g., USBR_BLANK_SPIKE). USBR_QULFR_DESCRIPTION is the quality assurance description (if any) (e.g., Result may have a high bias.).

This dataset includes dissolved oxygen data collected at 5-min intervals over a 24-hour period at three agricultural streams: Maple Creek in NE (2004), Morgan Creek in Delaware (2004) and Stalker Creek in Idaho (2007).

This dataset includes dissolved oxygen data collected at 5-min intervals over a 24-hour period at three agricultural streams: Maple Creek in NE (2004), Morgan Creek in Delaware (2004) and Stalker Creek in Idaho (2007).