This metadata document describes the data file 'model_recalibration_results.csv' for the recalibration of the Sinking Pond hydrologic model. The hydrologic model was recalibrated using the R script ‘annotated_R_script_for_model_recalibration.R’ and the input dataset ‘Daily_values_for_climate_stage_and_model_terms.csv’ in this data release to produce recalibrated values for the four calibrated parameters in the model: KBR, KPR, KBG, and PG (see the ‘Entity and Attribute’ section of this metadata document; for additional information see table 9 in Wolfe and others, 2004). The data file ‘model_recalibration_results.csv’ contains the results from the recalibration process. Each row in the dataset represents a different model, where each model is defined by a unique combination of values for KBR, KPR, KBG, and PG. All possible combinations of KBR (15 parameter values), KPR (13 parameter values), KBG (20 parameter values), and PG (21 parameter values) were run as separate models, to yield a total of 81,900 models (i.e., 81,900 rows in this dataset). Each model produced a time series of modeled pond stage which was compared to observed pond stage on a daily time step across 22 water years to calculate four model performance statistics: (1) root mean-squared error (RMSE), (2) Nash-Sutcliffe Efficiency (NSE), (3) the 25th percentile of NSE values across water years (NSE_p25), and (4) hydroperiod discrepancy (HPD); see the ‘Entity and Attribute’ section of this metadata document for additional information about model performance metrics.

The datasets provided contain modelled daily streamflow, and storage volume data for several NSW river systems. These data were generated by simulating baseline river system models used to inform the development of Regional Water Strategies. The models were simulated for three different climate scenarios: instrumental climate (about 130 years), paleo-stochastic climate (about 10,000 years), and paleo-stochastic climate with climate projection based on NARCliM 1.0 (about 10,000 years). Each modelled output is published as a ZIP file which contains two pdf files (.pdf) and three time series data (.csv). For more information on the NSW regional water strategies program, please refer to the following website. https://www.dpie.nsw.gov.au/water/our-work/plans-and-strategies/regional-water-strategies The naming structure of the individual zip files is "Watercourse at Gauge name, followed by Gauge number". 1) Bumbuggan Creek at Offtake Gauge 412017_NARCliM 2) Fairholme Gauge 412023_NARCliM 3) Goobang Creek at Condobolin Gauge 412014_NARCliM 4) Lachlan River at Belubula Gauge 412033_NARCliM 5) Lachlan River at BooberoiWeir Gauge 412021_NARCliM 6) Lachlan River at Booligal Gauge 412005_NARCliM 7) Lachlan River at Cargel Gauge 412011_NARCliM 8) Lachlan River at CondobolinWeir Gauge 412034_NARCliM 9) Lachlan River at Corrong Gauge 412045_NARCliM 10) Lachlan River at Cowra Gauge 412002_NARCliM 11) Lachlan River at DSJemalong Weir Gauge 412036_NARCliM 12) Lachlan River at Forbes Gauge 412004_NARCliM 13) Lachlan River at Hillston Weir Gauge 412039_NARCliM 14) Lachlan River at Nanami Gauge 412057_NARCliM 15) Lachlan River at Oxley Gauge 412026_NARCliM 16) Lachlan River at Reids Flat Gauge 412027_NARCliM 17) Lachlan River at USWillandraWeir Gauge 412038_NARCliM 18) Lachlan River at Whealbah Gauge 412078_NARCliM 19) Lachlan River at Wyangala Gauge 412067_NARCliM 20) Wyangala Dam-Volume Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.

This data is for Figures 6 and 7 in the journal article. The data also includes the two EPANET input files used for the analysis described in the paper, one for the looped system and one for the block system. This dataset is associated with the following publication: Grayman, W., R. Murray , and D. Savic. Redesign of Water Distribution Systems for Passive Containment of Contamination. JOURNAL OF THE AMERICAN WATER WORKS ASSOCIATION. American Water Works Association, Denver, CO, USA, 108(7): 381-391, (2016).

Physical and compositional characteristics of drinking water storage tank sediment. This dataset is associated with the following publication: Lytle, D., C. Muhlen, S. Lippitt, E. Crockett, and R. Achtemeier. Physical and compositional characteristics of drinking water storage tank sediment. AWWA Water Science. John Wiley & Sons, Inc., Hoboken, NJ, USA, 6(3): e1371, (2024).

Understanding and projecting the downstream benefits of terrestrial surface water storage (volumetric water stored in lakes and wetlands, SWstorage) requires watershed hydrologic models. Use of external datasets to calibrate and validate modeled SWstorage dynamics remains uncommon, particularly across major river basins. Here, we: (1) develop and assess the utility of a novel remote sensing-based (RS) SWstorage approach for verifying watershed-model SWstorage estimates, (2) compare average modeled and RS SWstorage volume across the landscape, and (3) compare variability in modeled and RS SWstorage through time. We used SWstorage informed by Sentinel-1 and -2 (RS SWstorage), with Soil and Water Assessment Tool (SWAT) model simulations (SWAT SWstorage) across the ~450,000 km2 Upper Mississippi River Basin. We found that RS SWstorage was, on average, lower than SWAT SWstorage in tile-drained agricultural regions where static Digital Elevation Model (DEM)-generated depressions used in the SWAT model often did not contain RS surface water. Conversely, RS SWstorage was higher than SWAT SWstorage in wetland-rich regions where surface water was shallower than DEM vertical accuracy. In modeled subbasins where DEM-generated maximum SWstorage capacity was low relative to SWAT SWstorage volumes, SWAT SWstorage was effectively capped and unable to vary through time, whereas RS SWstorage in the same subbasins continued to vary. Thus, RS SWstorage allows for a more accurate representation of where, when, and how much water is on the landscape. This finding is useful for informing watershed model initial conditions and highlights the potential for RS to be used in SWstorage calibration or data assimilation.

This dataset is the basis for the U.S. Geological Survey Scientific Investigations Report "An evaluation of methods for computing annual water quality loads", which utilized available data from Heidelberg University and the U.S. Geological Survey to evaluate the accuracy of various methods for computing annual water-quality loads. This dataset contains two files used in the report: "QW_FLOW.csv", which contains the water-quality sample and streamflow data used to develop water-quality load estimates, and "QW_LOAD.csv", which contains observed and estimated annual loads.

This dataset is the basis for the U.S. Geological Survey Scientific Investigations Report "An evaluation of methods for computing annual water quality loads", which utilized available data from Heidelberg University and the U.S. Geological Survey to evaluate the accuracy of various methods for computing annual water-quality loads. This dataset contains two files used in the report: "QW_FLOW.csv", which contains the water-quality sample and streamflow data used to develop water-quality load estimates, and "QW_LOAD.csv", which contains observed and estimated annual loads.

We developed four 2011-2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long-term planning for the city of Minneapolis, MN. Baseline land cover was from the NLCD2011 database. IPCC SRES scenarios (https://www.ipcc.ch/site/assets/uploads/2018/03/sres-en.pdf) interpreted by Sohl et al. (2014) (https://doi.org/10.1890/13-1245.1) for the conterminous United States were downscaled from 250 meters to 30 meters. The baseline and scenario land cover data were used as input into the Soil Water and Assessment Tool (SWAT) model to assess the effect of outyear projected land cover change on the source of raw water for the city of Minneapolis, MN. SWAT was used to assess the effect of land cover change on raw water concentrations of sediment, total nitrogen, and total phosphorus. The IPCC SRES evaluated were A1B, A2, B1, and B2. The four scenarios were implemented with (A1Bf) and without (A1B) forest recovery (e.g., conversion of cropland (2011) to forest (2050). Data on treatment of raw (source) water quality, provided by the city of Minneapolis, MN, were used in autoregressive models to determine if there was a temporal trend in mass of treatment chemicals applied. Models were run separately for each treatment chemical. Data are monthly application rates from 2008 through 2017. The day of the month for the date variable was nominally set to one (1). Data for alum were incomplete from 2008 through 2011, which were set to zero (0) and treated as missing in the autoregressive model. Water volume treated is in megagallons (Mg); 1 Mg = 1000 gallons. A dummy variable for change in management philosphy was included in the model. The dummy variable was set to zero (0) for the period 2008 - 2014 and one (1) afterward. The dummy variable is not included in the file. It had a significant effect only for the CO2 treatment chemical.

Simulation results and the figures in the manuscript that are generated. This dataset is associated with the following publication: Shang, F., J. Burkhardt, and R. Murray. Random Walk Particle Tracking to Model Dispersion in Steady Laminar and Turbulent Pipe Flow. JOURNAL OF HYDRAULIC ENGINEERING. American Society of Civil Engineers (ASCE), Reston, VA, USA, 149(7): 04023022, (2023).

This file contains the water quality data summarized in the 2003 report entitled: Baseline Water Quality Inventory, Fort Sumter National Monument. The data and locations existed in the United States Environmental Protection Agency's Storage and Retrieval (STORET) water quality database management system when the report was published.