Dataset of the measurements presented in the futureEnergy D8 submission

This folder contains the calibrationstatistics from DK-model2019. Grid files and .shp files are assembled in ArcGIS Pro v.3.0.1. DKmodel2019 setup and calibration is described in GEUS report 2019/31.

Aiming at the problem that it is difficult to accurately calibrate massive Pan-Tilt-Zoom Camera (PTZ) cameras on telecommunication tower and the visualization effect of orthographic geo-image is poor, this paper proposes a new method of realtime orthographic geo-image generating, which is considering Digital Elevation Model (DEM) and semantic information (ROGI-DS). First, through integrating tower cameras with 3D GIS, a camera calibration method based on view fitting (3D GIS-GeoC) is designed. Then, using the trained semantic segmentation model (TCSM), the sky area can automatically be identified and removed. Finally, based on the results of camera calibration and viewshed analysis, and the orthographic geo-image are generated. The results show that: (1) 3D GIS-GeoC method outperforms the traditional Perspective-n-Point (PnP) algorithm;(2) The tower camera semantic segmentation model (TCSM) achieves an accuracy of 96.7%; (3) ROGI-DS method improves the accuracy and visualization of orthographic geo-image under different terrain constraints, and can be used real-time monitoring of natural resources and emergency reliefs.

Discover the booming SF6 Gas Density Relay Calibrator market! This comprehensive analysis reveals key trends, growth drivers, regional breakdowns, and leading companies shaping this crucial sector for power grid maintenance. Learn about market size, CAGR, and future projections in our insightful report.

ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

Calibration of tested models ordered according to the performance indicator (RMSEs and proportionality between the RMSEs).

This data release contains inputs for and outputs from hydrologic simulations for the Hawai‘i (HI) domain using the Precipitation Runoff Modeling System (PRMS) version 5.2.1.1 and the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018). This child item holds GIS layers and methodology to make mean-monthly calibration targets (baselines) for the HI domain, as was used in the "byHRU" calibration. Global Circulation Model (GCM) data from National Aeronautics and Space Administration (NASA) were processed into 10 mean-monthly global datasets (Koczot and others, 2025) and stored in NetCDF format ( .nc file extension; https://www.unidata.ucar.edu/software/netcdf/ ). Using these datasets, mean-monthly values were computed for each hydrologic response unit (HRU) in the HI domain (Bock and others, 2024). Resulting data are used as calibration targets for the HI NHM-PRMS model application. DATA TYPE DESIGNATION: Data layers are labeled according to type, as shown below: ID Data Type 1. AET Actual evapotranspiration 2. GW Groundwater (really baseflow) component of total runoff. 3. PET Potential evapotranspiration 4. SCA Snow-cover area (fraction of cell area) from NASA FLDAS. 5. SM Soil moisture 6. SR Solar radiation 7. SRO Surface runoff 8. SSR Subsurface runoff. 9. SWE Snow-water equivalent 10. SWI Soil-water infiltration CONTENTS OF THIS CHILD PAGE: 1. HI_HRU_baseline_targets.zip = Folder containing the 10 mean-monthly calibration target shapefiles files used in the by-HRU calibration step for each of the data types defined above.

The 1988 WAC Humboldt County aerial photos were flown at a scale of 1:24000 on 3-30-1988. The photo frames were scanned at 600 dpi and then orthorectified in ERDAS 9.0 using photo identifiable points on the 2005 National Agriculture Inventory Program image. No camera calibration information was able to be acquired before the orthorectification so internal orientation information was calculated using methods described in the Yusuke Niwa paper "Creating Orthorectified Aerial Photography Without A Camera Calibration Report". This dataset is reference to the UTM Zone 10 coordinate system, units are in meters.This dataset was developed as part of the Humboldt Bay / Eel River Delta Historic Atlas project by McBain and Trush Inc. It is intended to give a general representation of spatial landscape changes over time. This data set is not designed for use as a primary regulatory tool in permitting or citing decisions, but may be used as a reference source. This is public information and may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application.

Stormwater Model Calibration Services Market Outlook

According to our latest research, the global stormwater model calibration services market size reached USD 1.04 billion in 2024, reflecting the industry’s increasing prioritization of precision in stormwater management. The market is expected to grow at a robust CAGR of 7.2% from 2025 to 2033, with the forecasted market size projected to reach USD 1.97 billion by 2033. This impressive growth is primarily driven by the rising frequency of extreme weather events, urbanization, and the tightening of environmental regulations, which collectively underscore the necessity for accurate hydrologic and hydraulic modeling across global urban and rural landscapes.

A major growth factor for the stormwater model calibration services market is the escalating global trend towards urbanization. As cities expand and impervious surfaces multiply, effective stormwater management has become a critical concern for municipalities and developers alike. Urban areas are increasingly susceptible to flash floods, waterlogging, and infrastructure damage, necessitating the deployment of advanced modeling and calibration solutions. Stormwater model calibration services enable authorities and engineering firms to accurately simulate hydrological and hydraulic behaviors, optimize drainage networks, and mitigate flood risks. Moreover, as urban planning integrates sustainability and resilience, the demand for precise calibration services is set to rise in tandem with the expansion of smart city initiatives and green infrastructure projects.

Another significant driver is the growing stringency of environmental regulations and compliance requirements. Governments and regulatory bodies worldwide are imposing stricter standards for water quality, runoff management, and ecosystem protection. Adherence to these regulations requires municipalities, industries, and engineering consultants to undertake sophisticated stormwater modeling, backed by rigorous calibration against real-world data. This has fueled investments in calibration services that ensure models are reliable, defensible, and capable of supporting regulatory submissions. Furthermore, the increasing adoption of digital twins and integrated water management platforms is amplifying the need for high-fidelity calibration, as these technologies depend on accurate models to deliver actionable insights for water resource management and climate adaptation.

The proliferation of advanced technologies, such as remote sensing, IoT-enabled sensors, and machine learning, is also catalyzing market growth. These innovations are transforming the way hydrologic and hydraulic data are collected, processed, and utilized for model calibration. Real-time monitoring and big data analytics are enabling service providers to deliver more accurate and dynamic calibration services, enhancing the predictive capabilities of stormwater models. Additionally, the integration of GIS-based tools and cloud computing is making calibration services more accessible, scalable, and cost-effective for a broader range of stakeholders, from small municipalities to large industrial complexes. As these technological advancements become mainstream, the stormwater model calibration services market is poised for sustained expansion.

Regionally, North America continues to dominate the market, driven by substantial investments in urban infrastructure, a mature regulatory framework, and a high concentration of engineering expertise. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid urbanization, increasing climate variability, and significant government initiatives to upgrade water management systems. Europe follows closely, benefiting from progressive environmental policies and a strong focus on sustainable urban development. Meanwhile, Latin America and the Middle East & Africa are witnessing growing adoption, albeit from a lower base, as governments and industries recognize the need for resilient stormwater management practices in the face of climate change and urban expansion.

Service Type Analysis

The stormwater model calibration services market is segmented by service type into hydrologic calibration, hydraulic calibration, water quality calibration, and others. Hydrologic calibration remains the backbone of the market, as it involves the adjustment of model parameters to accurately simulate rainfall-runoff processes. This service is crucial for urban drainage and flood risk assessment projects, ensurin

Abstract

This dataset contains the excel spreadsheet extract and shapefile of all NAM coal deposits and resources.

Namoi AWRA-R model implementation.

The metadata within the dataset contains the workflow, processes, input and output data and instructions to implement the Namoi AWRA-R model for model calibration or simulation. In Namoi the AWRA-R simulation was done twice, firstly without the baseflow input from the groundwater modelling and then second set of runs were carried out with the input from the groundwater modelling.

Each sub-folder in the associated data has a readme file indicating folder contents and providing general instructions about the workflow performed.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Namoi bioregion is provided in BA NAM 2.6.1.3 and 2.6.1.4 products.

'..\AWRAR_Metadata_WGW\Namoi_Model_Sequence.pptx' shows the AWRA-L/R modelling sequence.

Dataset History

The directories within contain the input data and outputs of the Namoi AWRA-R model for model calibration and simulation. The folders of calibration is used as an example,simulation uses mirror files of these data, albeit with longer time-series depending on the simualtion period.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Namoi subregion is provided in BA NAM 2.6.1.3 and 2.6.1.4 products.

Additional data needed to generate some of the inputs needed to implement AWRA-R are detailed in the corresponding metadata statement as stated below.

Here is the parent folder:

'..\AWRAR_Metadata_NGW\..'

Input data needed:

1. Gauge/node topological information in '...\model calibration\NAM5.3.1_low_calib\gis\sites\AWRARv5.00_reaches.csv'.

2. Look up table for soil thickness in '...\model calibration\NAM5.3.1_low_calib\gis\ASRIS_soil_properties\NAM_AWRAR_ASRIS_soil_thickness_v5.00.csv'. (check metadata statement)

3. Look up tables of AWRA-LG groundwater parameters in '...\model calibration\NAM5.3.1_low_calib\gis\AWRA-LG_gw_parameters\'.

4. Look up table of AWRA-LG catchment grid cell contribution in '...model calibration\NAM5.3.1_low_calib\gis\catchment-boundary\AWRA-R_catchment_x_AWRA-L_weight.csv'. (check metadata statement)

5. Look up tables of link lengths for main river, tributaries and distributaries within a reach in \model calibration\NAM5.3.1_low_calib\gis\rivers\'. (check metadata statement)

6. Time series data of AWRA-LG outputs: evaporation, rainfall, runoff and depth to groundwater.

7. Gridded data of AWRA-LG groundwater parameters, refer to explanation in '...'\model calibration\NAM5.3.1_low_calib\rawdata\AWRA_LG_output\gw_parameters\README.txt'.

8. Time series of observed or simulated reservoir level, volume and surface area for reservoirs used in the simulation: Keepit Dam, Split Rock and Chaffey Creek Dam.

   located in '...\model calibration\NAM5.3.1_low_calib\rawdata\reservoirs\'.

9. Gauge station cross sections in '...\model calibration\NAM5.3.1_low_calib\rawdata\Site_Station_Sections\'. (check metadata statement)

10. Daily Streamflow and level time-series in'...\model calibration\NAM5.3.1_low_calib\rawdata\streamflow_and_level_all_processed\'.

11. Irrigation input, configuration and parameter files in '...\model calibration\NAM5.3.1_low_calib\inputs\NAM\irrigation\'.

These come from the separate calibration of the AWRA-R irrigation module in:

'...\\irrigation calibration simulation\\', refer to explanation in readme.txt file therein.

1. For Dam simulation script, read the following readme.txt files

' ..\AWRAR_Metadata_NGW\dam model calibration simulation\Chaffey\readme.txt'

'... \AWRAR_Metadata_NGW\dam model calibration simulation\Split_Rock_and_Keepit\readme.txt'

Relevant ouputs include:

1. AWRA-R time series of stores and fluxes in river reaches ('...\AWRAR_Metadata_NGW\model calibration\NAM5.3.1_low_calib\outputs\jointcalibration\v00\NAM\simulations\')

   including simulated streamflow in files denoted XXXXXX_full_period_states_nonrouting.csv where XXXXXX denotes gauge or node ID.

2. AWRA-R time series of stores and fluxes for irrigation/mining in the same directory as above in files XXXXXX_irrigation_states.csv

3. AWRA-R calibration validation goodness of fit metrics ('...\AWRAR_Metadata_NGW\model calibration\NAM5.3.1_low_calib\outputs\jointcalibration\v00\NAM\postprocessing\')

   in files calval_results_XXXXXX_v5.00.csv

Dataset Citation

Bioregional Assessment Programme (2017) Namoi AWRA-R model implementation (pre groundwater input). Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/433a27f1-cee8-499e-970a-607c6a25e979.

Dataset Ancestors

• Derived From Historical Mining Footprints DTIRIS NAM 20150914

• Derived From Namoi AWRA-L model

• Derived From River Styles Spatial Layer for New South Wales

• Derived From Namoi Surface Water Mine Footprints - digitised

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From National Surface Water sites Hydstra

• Derived From Namoi AWRA-R (restricted input data implementation)

• Derived From Namoi Hydstra surface water time series v1 extracted 140814

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Namoi Existing Mine Development Surface Water Footprints

Abstract The dataset was derived by the Bioregional Assessment Programme from the National Suface Water sites Hydstra. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This is an ArcGIS point shape file, and it shows the location of 8 stream gauging sites within the Galilee subregion. ## Purpose Data from these stream gauging sites were used to calibrate the AWRA-L hydrological model ## Dataset History This dataset was created using ArcMap Tools. It has been created from the National Surface Water Sites Hydstra by selecting eight stream gauges that were used for Galilee surface water numerical model calibration. ## Dataset Citation Bioregional Assessment Programme (2015) Stream gauges for the Galilee surface water model calibration. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/84d81a05-9385-4da0-b64e-96f7de3c7e66. ## Dataset Ancestors * Derived From National Surface Water sites Hydstra

A new groundwater flow model for western Chippewa County, Wisconsin has been developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS). An analytic element GFLOW model was constructed and calibrated to generate hydraulic boundary conditions for the perimeter of the more detailed three-dimensional MODFLOW-NWT model. This three-dimensional model uses the USGS MODFLOW-NWT finite difference code, a standalone version of MODFLOW-2005 that incorporates the Newton (NWT) solver. The model conceptualizes the hydrogeology of western Chippewa County as a six-layer system which includes several hydrostratigraphic units. The model explicitly simulates groundwater-surface-water interaction with streamflow routing. Model input included recent estimates of aquifer hydraulic conductivities and a spatial groundwater recharge distribution developed using a GIS-based soil-water-balance model for the study area. Groundwater withdrawals from pumping were simulated for 269 high-capacity wells across the entire model domain, which includes western Chippewa County and portions of eastern Dunn County and southeastern Barron County. Model calibration used the parameter estimation code PEST, and calibration targets included heads and stream flows. Calibration f focused on the period from during 2011 to 2013 when the largest amount of calibration data were available. Following calibration, the model was applied to two distinct scenarios; one evaluating hydraulic impacts of more intensive industrial sand mining and the second evaluating the hydraulicimpacts of more intensive agricultural irrigation practices. Each scenario was developed with input by Chippewa County and a stakeholder group established for this study, and designed to represent reasonable future build-out conditions for both mining and irrigatedagriculture. The mining scenario underscores the potential hydraulic impacts related to changing land-use practices (i.e., hilltops and farm land becoming sand mines), while the irrigated agriculture scenario illustrates the potential hydraulic impacts of intensifying existing land-use practices (i.e., installing new wells to irrigate farm fields).

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This metadata contains data for two models: AWRA-L and AWRA-R. In the Macquarie-Tuggerah-Lake (MTL) coastal subregion, only AWRA-L modelling was conducted; in the Hunter subregion both modelling were conducted and AWRA-L flow outputs provided as model inputs for AWRA-R.

AWRA-L

The metadata within the dataset contains the workflow, processes, inputs and outputs data. The workflow pptx file under the top folder provides the top level summary of the modelling framework, including three slides. The first slide explains how to generate global definition file; the second slide outlines the calibration and simulation for AWRA-L model run; the third slide shows AWRA-L model post-processing for getting streamflow under baseline and coal mine resources development.

The exectable model framework is under the Application subfolder

Other subfolders, including model calibration, model simulation, post processing, contain the associated files used for model calibration, simulation and post processing, respectively.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

AWRA-R

The metadata within the dataset contains the workflow, processes, input and output data and instructions to implement the Hunter AWRA-R model for model calibration or simulation.

Each sub-folder in the associated data has a readme file indicating folder contents and providing general instructions about the workflow performed.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Purpose

BA surface water modelling in the Hunter bioregion

Dataset History

There are two sections, the first section describing AWRA-L, the second section describing AWRA-R.

Section 1 - AWRA-L

The directories within contain the input and output data of the Hunter AWRA-L model for model calibration, simulation and post-processing.

The calibration folder contains the input and output subfolders used for two model calibration schemes: lowflow and normal. The lowflow model calibration puts more weight on median and low streamflow; the normal model calibration puts more weight on high streamflow.

The simulation folder contains only one replicate of model input and output as an example.

The post-processing folder contains three subfolders: inputs, outputs and scripts used for generating streamflow under the baseline and coal mine resources development conditions. It contains the post-processing for the two subregions (MTL and HUN). In the MTL coastal subregion, the AWRA-L postprocessing results were final outputs, while in the HUN subregion AWRA-L flow outputs were model inputs for AWRA-R (Details below).

Input and output files are the daily data covering the period of 1953 to 2102, with the first 30 years (1953-1982) for model spin-up.

Documentation about the implementation of AWRA-L in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Data details are below

Model calibrations

1. Climate forcings are under '... AWRAL_Metadata\model calibration\inputs\Climate\'

2. Lowflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs\lowflow\'

3. Higflow calibration data including catchment location, global definition mapping, objective definition and optimiser definition under '... AWRAL_Metadata\model calibration\inputs ormal\'

4. Observed streamflow data used for model calibrations are under '... AWRAL_Metadata\model calibration\inputs\Streamflow\'

Model simulations

1. Climate forcings are under '... AWRAL_Metadata\model simulation\inputs\Climate\'

2. Global definition file used in csv output mode data is under '... AWRAL_Metadata\model simulation\inputs\csv_Model_1\'

3. Global definition file used in netcdf output mode data is under '... AWRAL_Metadata\model simulation\inputs\Netcdf_Model_1\'

4. Output files used in csv output mode data contain Dd, dgw, E0, Qg, Qtot, Rain, Sg outputs, which is used for AWRA-R model input and is under '... AWRAL_Metadata\model simulation\outputs\csv_Model_1\'

5. Output files used in netcdf output mode data contain Qg and Qtot outputs, which is used for AWRA-L postprocessing and is under '... AWRAL_Metadata\model simulation\outputs\Netcdf_Model_1\'

Post-processing

1. Input data include AWRA-L streamflow, ground water baseflow input and mine footprint data, stored at '... AWRAL_Metadata\post processing\Inputs\'

2. Output data include streamflow outputs under crdp and baseline for the HUN and MTL subregions, stored at '... AWRAL_Metadata\post processing\Outputs\'

3. Scripts for use for post-processing AWRA-L streamflow and ground water baseflow, is under '... AWRAL_Metadata\model simulation\post processing\Scripts\'

Section 2 - AWRA-R

The directories within contain the input data and outputs of the Hunter AWRA-R model for model calibration or simulation. The folders were calibration data stored is used as an example,

simulation uses mirror files of these data, albeit with longer time-series depending on the simualtion period.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Hunter bioregion is provided in BA HUN 2.6.1.3 and 2.6.1.4 products.

Additional data needed to generate some of the inputs needed to implement AWRA-R are detailed in the corresponding metadata statement as stated below.

Input data needed:

1. Gauge/node topological information in '...\model calibration\HUN4_low\gis\sites\AWRARv5.00_reaches.csv'.

2. Look up table for soil thickness in '...\model calibration\HUN4_low\gis\ASRIS_soil_properties\HUN_AWRAR_ASRIS_soil_thickness_v5.00.csv'. (check metadata statement)

3. Look up tables of AWRA-LG groundwater parameters in '...\model calibration\HUN4_low\gis\AWRA-LG_gw_parameters\'.

4. Look up table of AWRA-LG catchment grid cell contribution in '...model calibration\HUN4_low\gis\catchment-boundary\AWRA-R_catchment_x_AWRA-L_weight.csv'. (check metadata statement)

5. Look up tables of link lengths for main river, tributaries and distributaries within a reach in \model calibration\HUN4_low\gis\rivers\'. (check metadata statement)

6. Time series data of AWRA-LG outputs: evaporation, rainfall, runoff and depth to groundwater.

7. Gridded data of AWRA-LG groundwater parameters, refer to explanation in '...'\model calibration\HUN4_low\rawdata\AWRA_LG_output\gw_parameters\README.txt'.

8. Time series of observed or simulated reservoir level, volume and surface area for reservoirs used in the simulation: Glenbawn Dam and Glennies Creek Dam.

   located in '...\model calibration\HUN4_low\rawdata\reservoirs\'.

9. Gauge station cross sections in '...\model calibration\HUN4_low\rawdata\Site_Station_Sections\'. (check metadata statement)

10. Daily Streamflow and level time-series in'...\model calibration\HUN4_low\rawdata\streamflow_and_level_all_processed\'.

11. Irrigation input, configuration and parameter files in '...\AWRAR_Metadata\model calibration\HUN4_low\inputs\HUN\irrigation\'. These come from the separate calibration of the AWRA-R irrigation module in:

    '...\irrigation calibration\', refer to explanation in readme.txt file therein.

12. Dam simulation script '\AWRAR_Metadata\dam model calibration simulation\scripts\Hunter_dam_run_2.R' and configuration files in

    '\AWRAR_Metadata\dam model calibration simulation\scripts\Hunter_dam_config_2.csv'. The config file comes from a separate calibration of AWRA-R dam module in

    '\AWRAR_Metadata\dam model calibration simulation\', refer to the explanation in the readme.txt file therein

Relevant ouputs include:

1. AWRA-R time series of stores and fluxes in river reaches ('...\AWRAR_Metadata\model calibration\HUN4_low\outputs\jointcalibration\v01\HUN\simulations\')

   including simulated streamflow in files denoted XXXXXX_full_period_states_nonrouting.csv where XXXXXX denotes gauge or node ID.

2. AWRA-R time series of stores and fluxes for irrigation/mining in the same directory as above in files XXXXXX_irrigation_states.csv

3. AWRA-R calibration validation goodness of fit metrics ('...\AWRAR_Metadata\model calibration\HUN4_low\outputs\jointcalibration\v01\HUN\postprocessing\')

   in files calval_results_XXXXXX_v5.00.csv

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-LR Model v01. Bioregional Assessment Derived Dataset. Viewed 28 August 2018, http://data.bioregionalassessments.gov.au/dataset/670de516-30c5-4724-bd76-8ff4a42ca7a5.

Dataset Ancestors

• Derived From River Styles Spatial Layer for New South Wales

• Derived From HUN AWRA-L simulation nodes_v01

• Derived From Hunter River Salinity Scheme Discharge NSW EPA 2006-2012

• Derived From HUN AWRA-R simulation nodes v01

• Derived From Bioregional Assessment areas v06

• Derived From [GEODATA 9 second DEM and D8: Digital Elevation Model

Comparison of camera calibration results and errors.

Stormwater Model Calibration Services Market Outlook

According to our latest research, the global stormwater model calibration services market size was valued at USD 1.42 billion in 2024. The market is projected to grow at a CAGR of 8.1% over the forecast period, reaching an estimated USD 2.77 billion by 2033. This robust growth trajectory is primarily driven by increasing urbanization, the intensification of climate change impacts, and stricter regulatory frameworks surrounding water management and environmental protection. As cities expand and extreme weather events become more frequent, the demand for accurate and reliable stormwater model calibration services is surging globally.

One of the most significant growth factors for the stormwater model calibration services market is the rapid pace of urbanization worldwide. As urban areas expand, impervious surfaces such as roads, buildings, and parking lots increase, leading to higher volumes of stormwater runoff. This surge in runoff can overwhelm existing drainage systems, resulting in flooding, property damage, and environmental degradation. Municipalities and urban planners are increasingly relying on advanced stormwater modeling and calibration services to design resilient infrastructure, optimize drainage networks, and ensure compliance with evolving regulations. The integration of cutting-edge technologies, such as remote sensing, GIS, and real-time monitoring, has further enhanced the accuracy and efficiency of stormwater model calibration, making these services indispensable for modern urban development.

Another key driver fueling the market is the growing awareness of the environmental impacts of stormwater runoff. Unmanaged runoff can carry pollutants, sediments, and debris into natural water bodies, posing significant risks to aquatic ecosystems and public health. Governments and regulatory agencies around the world are enacting stringent water quality standards and requiring comprehensive environmental impact assessments for new infrastructure projects. This regulatory pressure is compelling municipalities, engineering firms, and industrial facilities to invest in high-quality stormwater model calibration services to demonstrate compliance and minimize their ecological footprint. The shift towards sustainable urban drainage systems (SUDS) and green infrastructure solutions has also boosted the adoption of integrated and water quality calibration services in recent years.

Technological advancements are reshaping the landscape of the stormwater model calibration services market. The adoption of machine learning, artificial intelligence, and cloud-based modeling platforms has revolutionized the way calibration is performed. These innovations enable faster data processing, real-time analytics, and more accurate simulations, thereby improving decision-making for flood risk assessment, infrastructure design, and environmental management. Furthermore, the increasing availability of high-resolution hydrological and meteorological data has enhanced the precision of model calibration, reducing uncertainties and enabling more effective stormwater management strategies. As technology continues to evolve, service providers are expected to offer more integrated and customized solutions to meet the diverse needs of end-users across various sectors.

From a regional perspective, North America and Europe currently dominate the stormwater model calibration services market, accounting for the largest share of global revenues. These regions benefit from well-established regulatory frameworks, significant investments in urban infrastructure, and a high level of technological adoption. However, the Asia Pacific region is emerging as a high-growth market, driven by rapid urbanization, increasing frequency of extreme weather events, and rising government initiatives for sustainable water management. Latin America and the Middle East & Africa are also witnessing growing demand, albeit at a slower pace, as awareness of flood risk and environmental protection continues to rise. Overall, the market is characterized by a dynamic regional landscape, with significant opportunities for expansion in both developed and developing economies.

In support of the 32nd Licence Round, the NSTA is releasing a rock physics study which has been conducted by Ikon Science. The study covers two parts of the UKCS – the Central North Sea (CNS) and the East Shetland Basin (ESB) and consists of the petrophysical and rock physics analysis of 45 wells and a seismic amplitude study of selected 3D seismic data sets. All of the deliverables from this project have been loaded to the Data Package tabs in the National Data Repository (NDR) and the user should download the data from there. The NSTA’s Well Data Availability layer has been updated to reflect which wells now have rock physics data available. The exception to this delivery via the NDR are the seismic attributes that have been derived from 3D seismic volumes owned by PGS. To gain free access to these derived seismic volumes, users will need to have licensed the underlying multi-client data owned by PGS and to this end, will need to contact PGS directly. For the CNS area, this relates to surveys PGS17003 and PGS16008. For the ESB area this relates to surveys PGS EOK-2012 and PGS ESB-10. Any issues with accessing these attribute volumes should be sent to nsta.correspondence@nstauthority.co.uk

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

AWRA-R model implementation.

The metadata within the dataset contains the workflow, processes, input and output data and instructions to implement the Namoi AWRA-R model for model calibration or simulation. In Namoi the AWRA-R simulation was done twice, firstly without the baseflow input from the groundwater modelling and then second set of runs were carried out with the input from the groundwater modelling.

Each sub-folder in the associated data has a readme file indicating folder contents and providing general instructions about the workflow performed.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Namoi bioregion is provided in BA NAM 2.6.1.3 and 2.6.1.4 products.

'..\AWRAR_Metadata_WGW\Namoi_Model_Sequence.pptx' shows the AWRA-L/R modelling sequence.

Purpose

BA Surface water modelling for Namoi bioregion

Dataset History

The directories within contain the input data and outputs of the Namoi AWRA-R model for model calibration and simulation. The folders of calibration is used as an example,simulation uses mirror files of these data, albeit with longer time-series depending on the simualtion period.

Detailed documentation of the AWRA-R model, is provided in: https://publications.csiro.au/rpr/download?pid=csiro:EP154523&dsid=DS2

Documentation about the implementation of AWRA-R in the Hunter bioregion is provided in BA NAM 2.6.1.3 and 2.6.1.4 products.

Additional data needed to generate some of the inputs needed to implement AWRA-R are detailed in the corresponding metadata statement as stated below.

Here is the parent folder:

'..\AWRAR_Metadata_WGW..'

Input data needed:

1. Gauge/node topological information in '...\model calibration\NAM5.3.1_low_calib\gis\sites\AWRARv5.00_reaches.csv'.

2. Look up table for soil thickness in '...\model calibration\NAM5.3.1_low_calib\gis\ASRIS_soil_properties\NAM_AWRAR_ASRIS_soil_thickness_v5.00.csv'. (check metadata statement)

3. Look up tables of AWRA-LG groundwater parameters in '...\model calibration\NAM5.3.1_low_calib\gis\AWRA-LG_gw_parameters\'.

4. Look up table of AWRA-LG catchment grid cell contribution in '...model calibration\NAM5.3.1_low_calib\gis\catchment-boundary\AWRA-R_catchment_x_AWRA-L_weight.csv'. (check metadata statement)

5. Look up tables of link lengths for main river, tributaries and distributaries within a reach in \model calibration\NAM5.3.1_low_calib\gis\rivers\'. (check metadata statement)

6. Time series data of AWRA-LG outputs: evaporation, rainfall, runoff and depth to groundwater.

7. Gridded data of AWRA-LG groundwater parameters, refer to explanation in '...'\model calibration\NAM5.3.1_low_calib\rawdata\AWRA_LG_output\gw_parameters\README.txt'.

8. Time series of observed or simulated reservoir level, volume and surface area for reservoirs used in the simulation: Keepit Dam, Split Rock and Chaffey Creek Dam.

   located in '...\model calibration\NAM5.3.1_low_calib\rawdata\reservoirs\'.

9. Gauge station cross sections in '...\model calibration\NAM5.3.1_low_calib\rawdata\Site_Station_Sections\'. (check metadata statement)

10. Daily Streamflow and level time-series in'...\model calibration\NAM5.3.1_low_calib\rawdata\streamflow_and_level_all_processed\'.

11. Irrigation input, configuration and parameter files in '...\model calibration\NAM5.3.1_low_calib\inputs\NAM\irrigation\'.

These come from the separate calibration of the AWRA-R irrigation module in:

'...\irrigation calibration simulation\', refer to explanation in readme.txt file therein.

1. For Dam simulation script, read the following readme.txt files

' ..\AWRAR_Metadata_WGW\dam model calibration simulation\Chaffey\readme.txt'

'... \AWRAR_Metadata_WGW\dam model calibration simulation\Split_Rock_and_Keepit\readme.txt'

Relevant ouputs include:

1. AWRA-R time series of stores and fluxes in river reaches ('...\AWRAR_Metadata_WGW\model calibration\NAM5.3.1_low_calib\outputs\jointcalibration\v00\NAM\simulations\')

   including simulated streamflow in files denoted XXXXXX_full_period_states_nonrouting.csv where XXXXXX denotes gauge or node ID.

2. AWRA-R time series of stores and fluxes for irrigation/mining in the same directory as above in files XXXXXX_irrigation_states.csv

3. AWRA-R calibration validation goodness of fit metrics ('...\AWRAR_Metadata_WGW\model calibration\NAM5.3.1_low_calib\outputs\jointcalibration\v00\NAM\postprocessing\')

   in files calval_results_XXXXXX_v5.00.csv

Dataset Citation

Bioregional Assessment Programme (2017) Namoi AWRA-R model implementation (post groundwater input). Bioregional Assessment Derived Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/8681bd56-1806-40a8-892e-4da13cda86b8.

Dataset Ancestors

• Derived From Historical Mining Footprints DTIRIS NAM 20150914

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Namoi Environmental Impact Statements - Mine footprints

• Derived From Namoi Surface Water Mine Footprints - digitised

• Derived From River Styles Spatial Layer for New South Wales

• Derived From National Surface Water sites Hydstra

• Derived From Namoi AWRA-L model

• Derived From Namoi Hydstra surface water time series v1 extracted 140814

• Derived From Namoi AWRA-R (restricted input data implementation)

• Derived From Namoi Existing Mine Development Surface Water Footprints

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Residual contributing catchments for selected stream gauges in the Hunter subregion. Generated from the Geoscience Austraila 9 second flow direction grid using selected stream gauges and model simulation nodes as pour points with the ArcGIS Watershed tool. Catchments exist as a shapefile file.

Purpose

Residual catchment boundaries are used in river system modelling.

Dataset History

Selected gauge locations were used as pour points to generate contributing catchment areas from the GA 9 second flow direction raster. The catchment delineation was achieved using the ArcGIS 10.1 Spatial Analyst WATERSHED tool from the Hydrology toolbox. Resultant watershed raster was also converted to a shapefile (no generalisation).

Dataset Citation

Bioregional Assessment Programme (XXXX) HUN AWRA-R calibration catchments v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d419aae0-1cb3-48a8-82de-941398a80e3a.

Dataset Ancestors

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From National Surface Water sites Hydstra

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From HUN AWRA-R calibration nodes v01

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From GEODATA TOPO 250K Series 3

• Derived From Victoria - Seamless Geology 2014

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

These datasets contain modeling files and GIS data associated with a risk assessment study for the Cambrian-Ordovician sandstone aquifer system in Illinois from predevelopment (1863) to the year 2070. Modeling work was completed using the Illinois Groundwater Flow Model, a regional MODFLOW model developed for water supply planning in Illinois, as a base model. The model is run using the graphical user interface Groundwater Vistas 7.0. The development and technical details of the base Illinois Groundwater Flow Model, including hydraulic property zonation, boundary conditions, hydrostratigraphy, solver settings, and discretization, are described in Abrams et al. (2018). Modifications to this base model (the version presented here) are described in Mannix et al. (2018), Hadley et al. (2020) and Abrams and Cullen (2020). Modifications include removal of particular multi-aquifer wells to improve calibration, changing Sandwich Fault Zone properties to achieve calibration at production wells within and near the fault zone, and the incorporation of demand scenarios based on a participatory modeling project with the Southwest Water Planning Group. The zipped folder of model files contains MODFLOW input (package) files, Groundwater Vistas files, and a head file for the entire model run. The zipped folder of GIS data contains rasters of: simulated drawdown in the St. Peter sandstone from predevelopment to 2018, simulated drawdown in the Ironton-Galesville sandstone from predevelopment to 2018, simulated head difference between the St. Peter and Ironton-Galesville sandstone units in 2018, simulated head above the top of the St. Peter sandstone for the years 2029, 2050, and 2070, and simulated head above the top of the Ironton-Galesville sandstone for the years 2029, 2050, and 2070. Raster outputs were derived directly from the simulated heads in the Illinois Groundwater Flow Model. Rasters are clipped to the 8 county northeastern Illinois region (Cook, DuPage, Grundy, Kane, Kendall, Lake, McHenry, and Will counties). Well names, historic and current head targets, and spatial offsets for the Illinois Groundwater Flow Model are available upon request via a data license agreement. Please contact authors to set this up if needed.

Peach Tree and Lower Surveyors Creek Flood Study All Other Required Data Peach Tree and Lower Surveyors Creek Flood Study - GIS Calibration (Historical Flood Marks and Historical Rainfall Data); Miscellaneous (Collected data, peak discharge locations, potential flood mitigation options, questionnaire responses, rainfall and stream gauges, remote sensing land use map, etc)