This dataset indicates the presence and persistence of water across Queensland between 1988 and 2022. Water is one of the world’s most important resources as it’s critical for human consumption, agriculture, the persistence of flora and fauna species and other ecosystem services. Information about the spatial distribution and prevalence of water is necessary for a range of business, modelling, monitoring, risk assessment, and conservation activities. The water count product is based on water index and water masks for Queensland (Danaher & Collett 2006) and represents the proportion of observations with water present across the Landsat time series as a fraction of total number of possible observations for the period 1 Jan 1988 to 31 Dec 2022. The product has two bands where band 1 is the number of times water was present across the time series, and band 2 is the count of unobscured (i.e. non-null) input pixels, or number of total observations for that pixel. Cloud, cloud-shadow, steep slopes and topographic shadow can obscure the ability to count water presence.

Evaporation, Transpiration, and Evapotranspiration Products for Australia based on the Maximum Entropy Production model (MEP). This record is an introduction of a method into the MEP algorithm of estimating the required model parameters over the entire continent of Australia through the use of pedotransfer function, soil properties and remotely sensed soil moisture data. The algorithm calculates the evaporation and transpiration over Australia on daily timescales at the 0.05 degree (5 km) resolution for 2003 – 2013. The MEP evapotranspiration (ET) estimates were validated using observed ET data from 20 Eddy Covariance (EC) flux towers across 8 land cover types in Australia and compared the MEP-ET at the EC flux towers with two other ET products over Australia; MOD16 and AWRA-L products. The MEP model outperformed the MOD16 and AWRA-L across the 20 EC flux sites, with average root mean square errors (RMSE), 8.21, 9.87 and 9.22 mm/8 days respectively. The average mean absolute error (MAE) for the MEP, MOD16 and AWRA-L were 6.21, 7.29 and 6.52 mm/8 days, the average correlations were 0.64, 0.57 and 0.61, respectively. The percentage bias of the MEP ET was within 20% of the observed ET at 12 of the 20 EC flux sites while the MOD16 and AWRA-L ET were within 20% of the observed ET at 4 and 10 sites respectively. The analysis showed that evaporation and transpiration contribute 38% and 62%, respectively, to the total ET across the study period which includes a significant part of the “millennium drought” period (2003 – 2009) in Australia. File naming conventions: E – Evaporation T – Transpiration ET – Evapotranspiration For the 8 day ET, Daily T and ET, the suffix nnn indicates day of year, for example: 001 for January 1, 145 for May 25 (leap year) or 26, etc. While for the daily E, the suffix is in the format mmdd (month,day) for example 0101 for January 1, 0525 for May 25.

Depth of the water table in the Daintree Rainforest, Cape Tribulation site between 2014 - 2015

This dataset comprises spatially and temporally dynamic estimates of the monthly latent heat flux (λE) and sensible heat flux (H) for all of Australia. The available energy (A, being net radiation [Rn] less the gound heat flux [G]) can be obtained by adding the λE and H datasets provided. Energy variables have been provided as hydrological equivalent units of depth, normalised to daily rates (mm/d). TERN OzFlux Surface Energy Balance (SEB) data were used to scale MODIS-based covariates of surface temperature less air temperature (Ts – Ta) and Rn using a Spatial and Temporal General Linear Model (ST-GLM) to third order. The ST-GLM SEB model was implemented across all of Australia on a 0.005° spatial grid (~ 500 m) on a monthly timestep from March 2000 through June 2023. Coefficients of the model were determined from the OzFlux network of eddy covariance flux tower data. Three flux tower sites were used to independently validate the accuracy of the model, being Calperum, SA, Howard Springs, NT, and Tumbarumba, NSW. The mean absolute difference (MAD) for λE, H and A was estimated as: 0.37, 0.39 and 0.34 mm/d, respectively. The relative errors determined by the MAD percentage (MADP) for λE, H, and A were estimated to be: 16%, 26%, and 9%, respectively. This dataset represents a new pathway for operational regional- to global-scale estimation of dynamic SEB variables.

This dataset indicates the presence and persistence of water across New South Wales between 1988 and 2012. Water is one of the world’s most important resources as it’s critical for human consumption, agriculture, the persistence of flora and fauna species and other ecosystem services. Information about the spatial distribution and prevalence of water is necessary for a range of business, modelling, monitoring, risk assessment, and conservation activities. For example, one of the necessary steps in the NSW State-wide Landcover and Trees Study (SLATS), which monitors vegetation change and is used in the production of vegetation maps, involves removing non-vegetative features such as water bodies through water masking. Water count The water count product is based on water index and water masks for NSW (Danaher & Collett 2006), and represents the proportion of observations with water present across the Landsat time series as a fraction of total number of possible observations in the 25yr period (1 Jan 1988 to 31 Dec 2012). The product has two bands where band 1 is the number of times water was present across the time series, and band 2 is the count of unobscured (i.e. non-null) input pixels, or number of total observations for that pixel. Cloud, cloud-shadow, steep slopes and topographic shadow can obscure the ability to count water presence. Water Prevalence The water prevalence product is extracted from the water count product and provides a measure of the relative persistence of water in the landscape (e.g. from always present to rarely and never present). There are 12 classes representing the percentage of time a pixel has had water present out of the total number of observations for that pixel (i.e Band 1/Band 2 of the water count product). Water prevalence mapping provides information for multiple, wide-reaching applications. For example, distance to locations of persistent water bodies can be modelled as a contributing indicator of potential biodiversity refugia. Files align with Landsat paths and rows (see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-tools), with files for water count denoted 'dd7' and water prevalence 'ddh'.

Measured water quality variables are reported for surface water from a permanent sampling site on Robson Creek, Far North Queensland. The variables include major cations and anions, trace heavy metals, solids (suspended and total) inorganic and organic nitrogen and phosphorus.

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.

<p>This dataset provides accurate, high-resolution (30 m) / high-frequency (monthly) / continuous (no gaps due to cloud) actual evapotranspiration (AET) for Australia using the CMRSET algorithm. The CMRSET algorithm uses reflective remotely sensed indices to estimate AET from potential evapotranspiration (PET; calculated using daily gridded meteorological data generated by the Bureau of Meteorology). Blending high-resolution / low-frequency AET estimates (e.g., Landsat and Sentinel-2) with low-resolution / high-frequency AET estimates (e.g., MODIS and VIIRS) results in AET data that are high-resolution / high-frequency / continuous (no gaps due to cloud) and accurate. These are all ideal characteristics when calculating the water balance for a wetland, paddock, river reach, irrigation area, landscape or catchment. </p><p> Accurate AET information is important for irrigation, food security and environmental management. Like many other parts of the world, water availability in Australia is limited and AET is the largest consumptive component of the water balance. In Australia 70% of available water is used for crop and pasture irrigation and better monitoring will support improved water use efficiency in this sector, with any water savings available as environmental flows. Additionally, ground-water dependent ecosystems (GDE) occupy a small area yet are "biodiversity hotspots", and knowing their water needs allows for enhanced management of these critical areas in the landscape. Having high-resolution, frequent and accurate AET estimates for all of Australia means this AET data source can be used to model the water balance for any catchment / groundwater system in Australia. </p><p> Details of the CMRSET algorithm and its independent validation are provided in Guerschman, J.P., McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Peña-Arancibia, J.L. and Chen, Y. (2022) Estimating actual evapotranspiration at field-to-continent scales by calibrating the CMRSET algorithm with MODIS, VIIRS, Landsat and Sentinel-2 data. Journal of Hydrology. 605, 127318, doi:10.1016/j.jhydrol.2021.127318</p> <p> <i>We strongly recommend users to use the TERN CMRSET AET V2.2</i>. Details of the TERN CMRSET AET V2.2 data product generation are provided in McVicar, T.R., Vleeshouwer, J., Van Niel, T.G., Guerschman, J.P., Peña-Arancibia, J.L. and Stenson, M.P. (2022) Generating a multi-decade gap-free high-resolution monthly actual evapotranspiration dataset for Australia using Landsat, MODIS and VIIRS data in the Google Earth Engine platform: Development and use cases. Journal of Hydrology (In Preparation).

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 Australian cosmic-ray soil moisture monitoring network was first established in 2010 to provide Australian and global researchers with spatially distributed intermediate scale soil moisture observations. A cosmic-ray sensor (CRS) provides continuous estimates of soil moisture over an area of approximately 30 hectares by measuring naturally generated fast neutrons (energy 10–1000 eV) that are produced by cosmic rays passing through the Earth’s atmosphere. The neutron intensity above the land surface is inversely correlated with soil moisture as it responds to the hydrogen contained in the soil and to a lesser degree to plant and soil carbon compounds. The cosmic-ray technique is also passive, non-contact, and is largely insensitive to bulk density, surface roughness, the physical state of water, and soil texture. The scale of CRS measurements fills the void between point scale sensor measurements and large scale satellite observations. The depth of measurements varies with the moisture content of the soil but is typically between 10-30 cm. The depth of observations is reported as ‘effective depth’. <br> The CosmOz network is expanding as new sensors are added over time. The initial network was funded by CSIRO Land and Water but more recently TERN has funded work to maintain the network add new sensors and deliver data more efficiently. The standard CRS installation includes; a cosmic-ray neutron tube, a rain gauge (2m high), temperature and humidity sensors, and an atmospheric pressure sensor. Measures of all parameters are reported at an hourly interval. Each CRS requires an in-field calibration across the footprint of measurements to convert neutron counts to soil moisture content. The calibration includes collection of soil samples for bulk density, lattice water content and soil organic carbon.<br> The Australia CosmOz network consists of <a href="https://cosmoz.csiro.au/sites">19 stations</a>. The extent of the network and available data can be seen at the CosmOz network web page: <a href="https://cosmoz.csiro.au/">https://cosmoz.csiro.au</a>. The data is also accessible from the <a href="https://landscapes-cosmoz-api.tern.org.au/rest/doc">TERN Cosmoz REST API</a>.<br> The calibration and correction procedures used by the network are described by <a href="https://doi.org/10.1002/2013WR015138">Hawdon et al. 2014 </a>.