Data describes habitat suitability modelling (HSM) results for fish in streams. The data was developed by University of Melbourne through the Melbourne Waterways Research Practice Partnership as part of the development of Melbourne Water’s Healthy Waterways Strategy 2018 (HWS2018). Analysis has been undertaken across the Melbourne Water operating region, where the operating region has been divided into 16,346 sub-catchments. Of these 16,346 subcatchments, 8233 contain Melbourne Water waterways. The results are presented for each of these 8233 reaches for these HWS scenarios:Current: habitat suitability for fish under current conditions (i.e. 2014).Current trajectory: habitat suitability for fish under urbanisation and climate change scenarios if current management approaches continue. Target trajectory: habitat suitability for fish given urbanisation and climate change (as for current trajectory), together with (a) delivery of performance objectives of the Healthy Waterways Strategy and (b) achievement of environmental condition scores as described in the Catchment Programs of the Healthy Waterways Strategy.Results are presented as:Stacked probabilities, i.e. habitat suitability all 13 native fished species added together. These stacked probability values were used in the HWS to provide a fish value score each reach and sub-catchments.Results are also provided for all 22 fish species. Presentation of habitat suitabilty model results for fish from the Healthy Waterways Strategy 2018.Habitat Suitability Model results have been thoroughly reviewed and are considered fit for purpose (i.e. for waterway planning). This data set covers the entire Melbourne Water region with the exception of very small areas close to Port Phillip Bay or Western Port. For example, there are small areas of French Island which are not captured.This data set was created using: 1. Streams dataset for the Healthy Waterways Strategy 2018 (developed by GraceGIS using Melbourne Water layers as inputs), and 2. Results from Habitat Suitability Modelling for the Healthy Waterways Strategy 2018. Further reading: Chee et al. (in development), Habitat Suitability Models, Scenarios and Quantitative Action Prioritisation (using Zonation) for Melbourne Water’s Healthy Waterways Strategy: A Resource Document, University of Melbourne and Melbourne Water for Melbourne Waterways Research Practice Partnership Melbourne Water (in development), Healthy Waterways Strategy Resource DocumentNOTE: Whilst every effort has been taken in collecting, validating and providing the attached data, Melbourne Water Corporation makes no representations or guarantees as to the accuracy or completeness of this data. Any person or group that uses this data does so at its own risk and should make their own assessment and investigations as to the suitability and/or application of the data. Melbourne Water Corporation shall not be liable in any way to any person or group for loss of any kind including damages, costs, interest, loss of profits or special loss or damage, arising from any use, error, inaccuracy, incompleteness or other defect in this data.

The U.S. Geological Survey, in cooperation with the Town of Barnstable, Massachusetts, modified an existing numerical, steady-state regional MODFLOW-2005 groundwater-flow model to evaluate changes in water levels from a reference condition (2015) for nine pumping and wastewater return flow scenarios prepared by the Hyannis Water System. The three-dimensional, steady-state groundwater-flow model used to simulate water level changes is a modified and recalibrated version of an existing model that was used to simulate the potential effects of sea-level rise on groundwater levels of the Sagamore and Monomoy freshwater lenses of the Cape Cod aquifer (Walter and others, 2016) (https://doi.org/10.3133/sir20165058). Two modifications, (1) the addition of spatially variable natural recharge from precipitation, and (2) a revised representation of wastewater return-flow recharge to septic systems in the Town of Barnstable, were made to the existing regional groundwater-flow model for this study. The modified model was recalibrated to the same observations of heads and streamflows as those used in the original model. The modifications and results of the recalibration are described in the appendix of the associated scientific investigations report (https://doi.org/10.3133/sir20195121). The model is a mathematical representation of the groundwater-flow system. Several assumptions and limitations of the modeling approach are discussed in the report, as well as in the scientific investigations report describing the original model (https://doi.org/10.3133/sir20165058). This USGS data release contains all the input and output files for the simulations described in the associated scientific investigations report (https://doi.org/10.3133/sir20195121). The modified model supersedes the original model described by Walter and others (https://doi.org/10.3133/sir20165058).

Wadeable stream habitat data from four long-term monitoring programs (AIM, AREMP, NRSA, PIBO MP) were obtained, pre-processed, transformed, and combined using R code following the Stream Habitat Metrics Integration (SHMI) Data Exchange Standard (Scully et al., 2023b). The dataset includes 26 stream habitat metrics collected between 2000 and 2022 across the United States at ~12,000 locations from ~19,000 data collection events for a total of ~200,000 measurements. Measurements include reach characteristics (sampled reach length, channel gradient, sinuosity), channel dimensions (bankfull width and height, average bankfull width to depth ratio, mean thalweg depth, average wetted width), channel substrate particle sizes (percent fines, percent bedrock, fine sediment percentiles), pools (residual pool depth, pool tail fines), bank characterizations (angle), and water quality/chemistry (specific conductance, pH, specific conductance, turbidity, total nitrogen, total phosphorous). The dataset consists of 4 csv files: 'RecordLevel.csv', 'Location.csv', 'Event.csv', and 'MeasurementOrFact.csv'. The 4 csv data tables may be linked in a database structure using the 'entity relationship diagram.jpg' or by linking the following: Join RecordLevel primary key 'datasetID' to Location foreign key 'datasetID'. Join Location primary key 'locationID to Event foreign key 'locationID'. Join Event primary key 'eventID' to MeasurementOrFact foreign key 'eventID'. An analysis-ready file ('AnalysisStreamHabitatMonitoringMetricDataset.csv') is also published for user convenience.

Wadeable stream habitat data from four long-term monitoring programs (AIM, AREMP, NRSA, PIBO MP) were obtained, pre-processed, transformed, and combined using R code following the Stream Habitat Metrics Integration (SHMI) Data Exchange Standard (Scully et al., 2023b). The dataset includes 26 stream habitat metrics collected between 2000 and 2022 across the United States at ~12,000 locations from ~19,000 data collection events for a total of ~200,000 measurements. Measurements include reach characteristics (sampled reach length, channel gradient, sinuosity), channel dimensions (bankfull width and height, average bankfull width to depth ratio, mean thalweg depth, average wetted width), channel substrate particle sizes (percent fines, percent bedrock, fine sediment percentiles), pools (residual pool depth, pool tail fines), bank characterizations (angle), and water quality/chemistry (specific conductance, pH, specific conductance, turbidity, total nitrogen, total phosphorous). The dataset consists of 4 csv files: 'RecordLevel.csv', 'Location.csv', 'Event.csv', and 'MeasurementOrFact.csv'. The 4 csv data tables may be linked in a database structure using the 'entity relationship diagram.jpg' or by linking the following: Join RecordLevel primary key 'datasetID' to Location foreign key 'datasetID'. Join Location primary key 'locationID to Event foreign key 'locationID'. Join Event primary key 'eventID' to MeasurementOrFact foreign key 'eventID'. An analysis-ready file ('AnalysisStreamHabitatMonitoringMetricDataset.csv') is also published for user convenience.

The dataset supported findings in the study: "Evaluation of SWAT reservoir, ponds, and wetlands tools in water and sediment simulation in the Rock River watershed". Results of this study demonstrate the impact of impoundments in SWAT modeling.The dataset includes sources of the SWAT input data. This dataset is associated with the following publication: Jalowska, A., and Y. Yuan. Evaluation of SWAT Impoundment Modeling Methods in Water and Sediment Simulations. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION. American Water Resources Association, Middleburg, VA, USA, 55(1): 209-227, (2019).