Water quality parameters of the surface water from the Robson Creek Rainforest site. The parameters include water temperature, conductivity, water pH, salinity and dissolved oxygen.

This data set contains information on Electrical Conductivity and pH from bore water from two plots, Blackbutt and Salmongum the Great Western Woodland Site.

Water quality parameters of the surface water from two permanent sampling sites on the Samford Creek, southeast Queensland, Australia, are determined. The parameters include water temperature, flow velocity, turbidity, major cations and anions, plus total inorganic and organic nitrogen and phosphorus.

The data set contains information on: sediment characteristics, univariate indices of the macrofauna community and ecosystem functions (net primary production, sediment oxygen consumption and nutrient cycling).

There are presence absence records for vegetation and matched hydrological data from 687 1 x 1 m quadrats recorded from 11 wetlands and wetland complexes (28 sampled hydrological gradients (referred to as transects) across the upper and lower southeast of South Australia. Plant data were collected in spring 2013. Hydrological monitoring data at each site consisted of continuous (6 hourly) surface water level data from a state agency monitoring network. Observed water levels at the monitoring instrument on the day of monitoring were related to the observed depth of water at each quadrat, assuming a flat, level water surface and obtain a datum for each quadrat relative to the monitoring instrument. The continuous monitoring record was then used to calculate a range of different hydrological predictors indicating the variation at each quadrat. The hydrological dataset provided are the univariate summary statistics recording different aspects of surface water dynamics for each quadrat. Hydrological predictors (sum-exceedance value, hydroperiod and maximum inundation depth) were calculated for annual and seasonal periods in the three-years prior to plant data collection. See metadata and relevant publication for additional details on calculation. Hydrological predictors for each quadrat are provided in a single matrix of sites by predictors, with relevant location details for the quadrat (xy coordinates, site, transect). Included is a single electrical conductivity class for each transect (ordinal variable - low moderate, high - see metadata). Vegetation data are provided as a single matrix (quadrats x plant functional group) showing presence absence of each functional group in each quadrat. There is also a lookup table giving the assignment of each plant species to a plant functional group.

River sites were sampled during the summers of 2008/09 and 2009/10 in a survey designed to identify correlations between commonly used river condition variables and grazing land-use. Potential stream sites in northern Tasmania were screened by catchment size, northing and slope, and according to attributes aimed at minimising confounding variables, maintaining broad consistency in landscape and geomorphological context, and promoting independence among sites. A set of 27 survey sites was selected across a gradient from low to high proportion of land under grazing in their upstream catchments. Catchment sizes varied from 20-120 km2 and proportion grazing from 0-80%. Macroinvertebrates were sampled using Surber sampler. All macroinvertebrates within a 20% sub-sample identified to family and counted, with individuals from the insect orders Ephemeroptera, Plecoptera and Trichoptera identified to genus/species (by Laurie Cook, UTAS). Algal abundance was estimated at each site as the proportion of algal cover and as areal density of benthic chlorophyll a. Physical data variables collected were: water temperature, conductivity, turbidity, pH, total alkalinity, nitrate+nitrate, dissolved reactive phosphorus, total nitrogen, total phosphorus, overhead shading, the proportion of fine sediments within the sampled riffle zone, accumulated abstraction index and accumulated regulation index. For more information see: See Magierowski RH, Read SM, Carter SJB, Warfe DM, Cook LS, Lefroy EC and Davies PE. Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks. PLOS ONE.

The NSW Forest Monitoring Steering Committee commissioned the University of Melbourne to deliver baselines, drivers and trends for water quality and quantity in the NSW Regional Forest Agreement (RFA) regions. Following this work, the University of Melbourne was asked to extend the analysis to cover all NSW forested catchments. Both the initial project (RFA regions) and the extension (all NSW forested catchments) are included in this publication.<br> This dataset contains the estimated Mann-Kendall trends (direction and significance) in seven water quality and six water quantity indicators. The trends were estimated using a temporal regression that included a linear trend, the flow effect, a seasonality component and a lag-1 autoregressive residual model for which water quality data were sampled at daily or higher frequencies. For each water quality variable, trends were estimated for catchments which have 50% catchment area covered by forest, and long-term data monitored at the outlet of each catchment. All trends were estimated with the full historical records of each variable at each catchment in RFA regions, and the extension across all NSW forested catchments also produced short term trends. More detailed metadata for each dataset is included.<br> The seven quality indicators are: total phosphorus (TP), total nitrogen (TN), dissolved oxygen (DO), pH, electrical conductivity (EC), turbidity and water temperature (WTemp). <br> The six quantity indicators are: annual flow, annual rainfall-runoff residual, annual high flow, annual low flow, annual 7-day (7d) low flow and annual cease to flow (CTF).<br> Water monitoring sites analysed included those from the WaterNSW, Bureau of Meteorology, Water Data Online (BoM WDO) and Forestry Corporation NSW (FCNSW).<br> A web mapping application on the NSW Spatial Collaboration Portal depicts these datasets. Access the webapp through the link below: <br> https://portal.spatial.nsw.gov.au/portal/home/item.html?id=03950cf226ac4d459b8c8e3631e17afb

These datasets consist of soil maps generated to assess baselines, drivers and trends for soil health and stability within the NSW Regional Forest Agreement (RFA) regions. <br> The maps are organised into empirical soil maps, digital soil maps, and data cube maps. <br> Empirical soil maps consists of four products. Maps include topsoil pH, carbon, Emerson Aggregate Stability and Soil Profile Quality Confidence. Each map consists of 2,162 units. Maps were generated using the most representative soil profile for each unit available within the Soil and Land Information System (SALIS). The 2008 woody vegetation coverage was used as baseline. Maps reflect values when the sampling occurred with temporal changes not being accounted for. Locations with missing or of poor quality data are identified, providing a confidence rating map as part of the evaluation process.<br> Digital soil maps include map products of key soil condition indicators covering the Regional Forest Agreement regions of eastern NSW. Raster maps of key soil indicators, such as soil carbon, pH, bulk density, hillslope erosion and others, were created at 100 m resolution. For each key soil indicator, maps include baseline (approximately 2008) levels as well as trends of change resulting from different human and natural disturbances such as forest harvesting, uncontrolled stock grazing, climate change and bush fire. <br> Data cube maps include time series of soil organic carbon (SOC) between January 1990 and December 2020 for the Regional Forest Agreement regions of eastern NSW. Products provide estimates of SOC concentrations and associated trends through time. Modelling was carried out using a data cube platform incorporating machine learning space-time framework and geospatial technologies. Important covariates required to drive this spatio-temporal modelling were identified using the Recursive Feature Elimination algorithm (RFE). <br> A web mapping application on the NSW Spatial Collaboration Portal depicts these datasets. Access the webapp through the link below:<br> https://portal.spatial.nsw.gov.au/portal/home/item.html?id=af9c71935f024f4a8f64cb39f5eba007