The hydrological software market, currently valued at $733 million in 2025, is projected to experience robust growth, driven by increasing demand for accurate and efficient water resource management solutions. The rising frequency and intensity of extreme weather events, coupled with growing concerns over water scarcity and pollution, are compelling governments and organizations to adopt sophisticated hydrological modeling tools. This market expansion is further fueled by advancements in technology, such as cloud computing and AI, which are enhancing the capabilities and accessibility of hydrological software. The integration of these technologies allows for more detailed simulations, better predictions of hydrological events, and improved decision-making processes. Key players like Gardenia, GeoHECHMS, and MIKE SHE are actively shaping this landscape through continuous innovation and strategic partnerships. The market is segmented based on software type (e.g., 2D/3D modeling, GIS integration), application (e.g., flood forecasting, water quality management), and user type (e.g., government agencies, consulting firms). The global nature of water resource challenges ensures that the market will witness significant growth across various regions, with North America and Europe anticipated to hold substantial market shares due to existing infrastructure and regulatory frameworks. Continued technological advancements, coupled with rising awareness of water resource management, will likely propel the CAGR of 8.1% throughout the forecast period (2025-2033). The competitive landscape is marked by a mix of established players and emerging technology providers. Established players leverage their extensive experience and comprehensive product portfolios to maintain market share. However, emerging companies are introducing innovative solutions and disrupting the market with advanced functionalities and cost-effective solutions. Future growth will hinge on the continued development of user-friendly interfaces, integration with other data sources, and the ability to effectively address the specific hydrological challenges of diverse geographic locations. The ongoing development of more sophisticated algorithms and the increasing availability of high-resolution data will further enhance the accuracy and reliability of hydrological models, solidifying the market's long-term growth trajectory. A focus on data security and user training will be crucial for wider adoption and market penetration.

This service is built by processing the analysis assimilation output of the National Water Model land based data. All output of the land dataset have the same geospatial extent covering all of CONUS with partial coverage into Canada and Mexico. Currently, the only layer within the service is near-surface soil moisture saturation. The near-surface soil moisture saturation layer shows moisture saturation of the top 40cm of the soil. Model Output Version: v2.1See https://water.noaa.gov/about/nwm for further details about this data.Link to graphical web page: https://water.noaa.gov/mapLink to data download: https://nomads.ncep.noaa.gov/pub/data/nccf/com/nwm/prod for raw data files in netcdf format.

River hydraulic geometry is an important input to hydraulic and hydrologic models that route flow along streams, determine the relationship between stage and discharge, and map the potential for flood inundation give the flow in a stream reach. Traditional approaches to quantify river geometry have involved river cross-sections, such as are required for input to the HEC-RAS model. Extending such cross-section based models to large scales has proven complex, and, in this presentation, an alternative approach, the Height Above Nearest Drainage, or HAND, is described. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM software (http://hydrology.usu.edu/taudem) to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter. Together with slope (also determined from the DEM) and roughness (Manning's n) these provide all the inputs needed for establishing a Manning's equation uniform flow assumption stage-discharge rating curve and for mapping potential inundation from discharge. This presentation will describe the application of this approach across the continental US in conjunction with NOAA’s National Water Model for prediction of stage and flood inundation potential in each of the 2.7 million reaches of the National Hydrography Plus (NHDPlus) dataset, the vast majority of which are ungauged. The continental US scale application has been enabled through the use of high performance parallel computing at the National Center for Supercomputing Applications (NCSA) and the CyberGIS Center at the University of Illinois.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/.

The global hydraulic modeling software market is experiencing robust growth, driven by increasing demand for efficient water resource management and the need for advanced infrastructure planning. The market's expansion is fueled by several factors, including escalating urbanization, climate change leading to unpredictable weather patterns and water scarcity, and the growing adoption of digital twin technology for improved infrastructure monitoring and optimization. Governments worldwide are investing heavily in upgrading water infrastructure and implementing smart water management systems, further stimulating demand for sophisticated hydraulic modeling solutions. This trend is expected to continue throughout the forecast period (2025-2033), with a projected Compound Annual Growth Rate (CAGR) of, let's assume, 8% based on industry trends and the observed growth in related sectors like GIS and infrastructure software. Key players like Bentley Systems, Siemens, and Innovyze are continuously innovating their offerings, incorporating advanced analytics, AI, and cloud-based solutions to enhance the capabilities of hydraulic modeling software, thereby attracting a broader customer base. Market segmentation plays a vital role in understanding this growth. While specific segment details are unavailable, we can infer several key segments driving growth: municipal water utilities (largest segment), industrial applications (e.g., power generation, manufacturing), and consulting firms specializing in water resource management. The adoption of cloud-based solutions is also a significant segmental trend, offering greater scalability, accessibility, and collaborative capabilities. Geographic growth is expected to be widespread, with North America and Europe remaining strong markets due to established infrastructure and regulatory frameworks. However, rapid urbanization and infrastructure development in Asia-Pacific and other developing regions present significant growth opportunities. While competition is intense, the market offers significant potential for continued expansion, driven by the global need for efficient and sustainable water resource management.

Accuracy comparison of different models.

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).

The hydrological services market, currently valued at $727 million in 2025, is projected to experience robust growth, driven by increasing concerns about water scarcity, climate change impacts, and the need for effective water resource management. The Compound Annual Growth Rate (CAGR) of 6.8% from 2025 to 2033 indicates a significant expansion of this market over the forecast period. Key drivers include the rising demand for accurate hydrological data for infrastructure planning (dams, irrigation systems, etc.), improved flood forecasting and risk mitigation, and the growing adoption of advanced technologies such as remote sensing, GIS, and hydrological modeling. Furthermore, stricter environmental regulations and increasing government investments in water infrastructure projects are fueling market expansion. Competitive pressures within the industry are shaping innovation, pushing providers to offer more comprehensive and integrated services. Major players like FloSolutions, Gomez and Sullivan, and others are investing in research and development to enhance their offerings and gain a competitive edge. The market is segmented by service type (e.g., hydrological modeling, data acquisition, flood risk assessment) and geographical region. While precise regional breakdowns are unavailable, it is reasonable to expect that regions facing water stress and those with significant investments in water infrastructure will represent the largest market segments. The market's growth, however, is not without its challenges. Restraints include the high cost of advanced hydrological technologies and the need for specialized expertise to effectively utilize these tools. Data scarcity in certain regions, and the complexity of hydrological modeling in diverse geographical contexts, can also pose barriers to market penetration. Nevertheless, the increasing awareness of water resource management challenges coupled with technological advancements is expected to outweigh these constraints, leading to sustained market expansion throughout the forecast period. The market is expected to see further consolidation with larger players acquiring smaller firms to expand their service offerings and geographical reach. This will increase competition and drive innovation within the market.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

This resource includes a single GeoPackage containing upstream basin boundaries for 8,175 USGS streamflow gages used for data assimilation by v3.0 of the National Water Model. The file contains vector geometry and a single attribute ("provider_id") that indicates the associated USGS site code. Basin geometry is important for estimating drainage area and deriving areal statistics, such as mean areal precipitation. The list of included USGS gages was drawn from the National Water Model RouteLink files located here: https://www.nco.ncep.noaa.gov/pmb/codes/nwprod

Note that these basin boundaries are unofficial and may deviate significantly from official USGS estimates of contributing drainage area. These basin boundaries were derived by accumulating NHDv2 Catchments using the HyRiver suite of Python tools. See the "Drainage Area Delineation" example for more details: https://docs.hyriver.io/examples/notebooks/nwis_catchments.html

Important Note: This item is in mature support as of November 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The National Water Model (NWM) is a new product from the National Weather Service that forecasts streamflow volume and velocity over the entire continental United States. It is a hydrologic model that predicts the flow in every river reach of the National Hydrography Dataset, mathematically modeling physical processes like snowmelt, infiltration and the movement of water through soil layers in order to determine how much of the NWS precipitation forecast becomes runoff, then routing that runoff through the river network. This is the short term forecast, which is run every hour, predicting streamflow over the next eighteen hours at one hour interval. What Can You Do With This Layer?This map service is designed for fast data visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the forecast data sequentially using the time slider, which is set to one hour intervals by default, by Enabling Time Animation. This layer type is not recommended for use in analysis. RevisionsSep 23, 2020: Updated 'qout' field values for Water Bodies. Null values are now being replaced with '-9999' in order to correct an identify issue at small scales. Also updated Pop-Up to reflect that the 'qout' value is Not Available (N/A).Nov 18, 2021: Updated Feature set to v2.1 of the NWM data. Added 'qnormal' field to provide expected monthly flow for given forecast.

Market Analysis of Hydraulic Modeling Software The global hydraulic modeling software market is projected to witness significant growth over the next decade, driven by the increasing demand for water management solutions. The market is estimated to have reached a size of USD 1.2 billion in 2025 and is forecast to expand at a CAGR of 5.4% during the period 2025-2033, reaching a value of USD 1.9 billion by 2033. The rising awareness of water conservation and the need for efficient distribution networks are key factors driving market growth. Additionally, the integration of advanced technologies such as GIS, CAD, and SCADA systems is enhancing the capabilities and accuracy of hydraulic modeling software. The market for hydraulic modeling software is segmented based on application, type, and region. Major applications include professional managed services, consulting services, and maintenance and support. Types include GIS hydraulic modeling software, simulation, and advanced pressure management. Regionally, North America and Europe dominate the market, with Asia-Pacific expected to witness the fastest growth over the forecast period. Key players in the market include Bentley Systems, Siemens Industry Software GmbH, and Innovyze, who are continuously innovating and developing advanced solutions to meet the evolving needs of the water industry. The global hydraulic modeling software market is expected to reach USD 436 million by 2026, from USD 270 million in 2019, exhibiting a CAGR of 7.1% during the forecast period. Emerging economies are expected to offer significant growth opportunities for global hydraulic modeling software providers, over the upcoming years. This growth can be attributed to the rising infrastructure development activities, increasing water scarcity, and stringent regulations for water conservation and management.

The global hydraulic modeling software market is highly competitive, with key players implementing various organic and inorganic growth strategies to gain market share. Leading players in the market include Bentley Systems (US), Siemens Industry Software GmbH (Germany), Innovyze (US), Hydraulic Analysis Group Limited (UK), Pannam Imaging Interface Solutions (India), Haestad Methods (US), Broomfield Colo. (US), Wallingford (US), MWH Global (US), IBM Corporation (US), and Ceinsys Tech Ltd (UK).

This dataset represents a water shortage vulnerability analysis performed by DWR using modified PLSS sections pulled from the Well Completion Report PLSS Section Summaries. The attribute table includes water shortage vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to modified PLSS sections. Several relevant summary statistics from the Well Completion Reports are included in this table as well. This data is from the 2024 analysis.

Water Code Division 6 Part 2.55 Section 8 Chapter 10 (Assembly Bill 1668) effectively requires California Department of Water Resources (DWR), in consultation with other agencies and an advisory group, to identify small water suppliers and “rural communities” that are at risk of drought and water shortage. Following legislation passed in 2021 and signed by Governor Gavin Newsom, the Water Code Division 6, Section 10609.50 through 10609.80 (Senate Bill 552 of 2021) effectively requires the California Department of Water Resources to update the scoring and tool periodically in partnership with the State Water Board and other state agencies. This document describes the indicators, datasets, and methods used to construct this deliverable.  This is a statewide effort to systematically and holistically consider water shortage vulnerability statewide of rural communities, focusing on domestic wells and state small water systems serving between 4 and 14 connections. The indicators and scoring methodology will be revised as better data become available and stake-holders evaluate the performance of the indicators, datasets used, and aggregation and ranking method used to aggregate and rank vulnerability scores. Additionally, the scoring system should be adaptive, meaning that our understanding of what contributes to risk and vulnerability of drought and water shortage may evolve. This understanding may especially be informed by experiences gained while navigating responses to future droughts.”

A spatial analysis was performed on the 2020 Census Block Groups, modified PLSS sections, and small water system service areas using a variety of input datasets related to drought vulnerability and water shortage risk and vulnerability. These indicator values were subsequently rescaled and summed for a final vulnerability score for the sections and small water system service areas. The 2020 Census Block Groups were joined with ACS data to represent the social vulnerability of communities, which is relevant to drought risk tolerance and resources. These three feature datasets contain the units of analysis (modified PLSS sections, block groups, small water systems service areas) with the model indicators for vulnerability in the attribute table. Model indicators are calculated for each unit of analysis according to the Vulnerability Scoring documents provided by Julia Ekstrom (Division of Regional Assistance).

All three feature classes are DWR analysis zones that are based off existing GIS datasets. The spatial data for the sections feature class is extracted from the Well Completion Reports PLSS sections to be aligned with the work and analysis that SGMA is doing. These are not true PLSS sections, but a version of the projected section lines in areas where there are gaps in PLSS. The spatial data for the Census block group feature class is downloaded from the Census. ACS (American Communities Survey) data is joined by block group, and statistics calculated by DWR have been added to the attribute table. The spatial data for the small water systems feature class was extracted from the State Water Resources Control Board (SWRCB) SABL dataset, using a definition query to filter for active water systems with 3000 connections or less. None of these datasets are intended to be the authoritative datasets for representing PLSS sections, Census block groups, or water service areas. The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR.

These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the Drought and Water Shortage Vulnerability Scoring: California's Domestic Wells and State Smalls Systems documentation for more information on indicators and scoring. These estimated indicator scores may sometimes be calculated in several different ways, or may have been calculated from data that has since be updated. Counts of domestic wells may be calculated in different ways. In order to align with DWR SGMO's (State Groundwater Management Office) California Groundwater Live dashboards, domestic wells were calculated using the same query. This includes all domestic wells in the Well Completion Reports dataset that are completed after 12/31/1976, and have a 'RecordType' of 'WellCompletion/New/Production or Monitoring/NA'.

Please refer to the Well Completion Reports metadata for more information. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.4, dated September 14, 2022. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data.

DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

The global Water Surface Profile Software market is experiencing robust growth, driven by increasing demand for efficient water resource management and infrastructure development. The market, estimated at $500 million in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $950 million by 2033. This growth is fueled by several key factors, including the rising adoption of advanced hydrological modeling techniques for flood prediction and mitigation, stringent regulatory compliance requirements for water infrastructure projects, and the increasing need for accurate water level forecasting to support irrigation and hydropower management. Furthermore, the integration of Geographic Information Systems (GIS) and other data analytics tools within these software platforms is enhancing their capabilities and market appeal. The market is segmented by software type (2D, 3D, 1D), deployment mode (cloud, on-premise), and application (flood management, irrigation, hydropower). Major players in the market, including Bentley Systems, Deltares, DHI Group, and Innovyze, are continuously investing in research and development to enhance software functionalities, expand their product portfolios, and explore new market opportunities. Competitive advantages are established through superior user interfaces, advanced modeling capabilities, data integration, and comprehensive customer support services. The market faces some restraints, including the high initial investment costs for software licenses and the need for skilled professionals to effectively utilize these sophisticated tools. However, the long-term benefits of improved water management and infrastructure planning are driving adoption, even in the face of these challenges. Future growth will be significantly shaped by government initiatives promoting sustainable water resource management, technological advancements in cloud computing and AI-powered analytics, and the increasing focus on climate change adaptation and mitigation strategies.

The National Water Model provides forecasts of flow volume and velocity for over 2.7 million stream and river segments in the contiguous United States and is the National Weather Service’s primary tool for predicting river flooding. Two versions of the National Water Model are available in ArcGIS Living Atlas: a short-term model that updates every hour for 18-hrs, and a medium-range model that updates every 6-hrs for a 10-day outlook.This layer provides a summary of the short-term hourly forecast and adds calculated fields such as flow anomaly, maximum anomaly, and the time at maximum flow. It has been filtered to display only areas with positive flow anomalies (i.e., flooding). Leveraging ArcGIS Online hosted feature services, it is ideal for doing spatial and time queries, use in Dashboards, and supporting a variety of custom symbology.By default, this layer is showing time until maximum flow since the short-term model is ideal for providing detailed situational awareness for imminent or occurring events.Companion LayerNational Water Model Maximum Flow (10-Day Forecast)Related LayersNational Water Model (10-Day Forecast)National Water Model (10-Day Anomaly Forecast)National Water Model (Hourly Forecast)National Water Model (Hourly Anomaly Forecast)RevisionsJan 27, 2022: Added 'Forecast Origin' field. Providing the Origin Date/Time of the Forecast Set

This is the authors’ version of the work. It is based on a poster presented at the Wageningen Conference on Applied Soil Science, http://www.wageningensoilmeeting.wur.nl/UK/ Cite as: Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L., 2011. Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map. Wageningen Conference on Applied Soil Science “Soil Science in a Changing World”, 18 - 22 September 2011, Wageningen, The Netherlands. Author’s version DOI:10.6084/m9.figshare.936872 arXiv:1402.3847

Towards the reproducibility in soil erosion modeling:a new Pan-European soil erosion map

Claudio Bosco ¹, Daniele de Rigo ¹ ² , Olivier Dewitte ¹, Luca Montanarella ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy

Soil erosion by water is a widespread phenomenon throughout Europe and has the potentiality, with his on-site and off-site effects, to affect water quality, food security and floods. Despite the implementation of numerous and different models for estimating soil erosion by water in Europe, there is still a lack of harmonization of assessment methodologies. Often, different approaches result in soil erosion rates significantly different. Even when the same model is applied to the same region the results may differ. This can be due to the way the model is implemented (i.e. with the selection of different algorithms when available) and/or to the use of datasets having different resolution or accuracy. Scientific computation is emerging as one of the central topic of the scientific method, for overcoming these problems there is thus the necessity to develop reproducible computational method where codes and data are available. The present study illustrates this approach. Using only public available datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate the most sensitive areas to soil erosion by water in Europe. A significant effort was made for selecting the better simplified equations to be used when a strict application of the RUSLE model is not possible. In particular for the computation of the Rainfall Erosivity factor (R) the reproducible research paradigm was applied. The calculation of the R factor was implemented using public datasets and the GNU R language. An easily reproducible validation procedure based on measured precipitation time series was applied using MATLAB language. Designing the computational modelling architecture with the aim to ease as much as possible the future reuse of the model in analysing climate change scenarios is also a challenging goal of the research.

References [1] Rusco, E., Montanarella, L., Bosco, C., 2008. Soil erosion: a main threats to the soils in Europe. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 37-45 [2] Casagrandi, R. and Guariso, G., 2009. Impact of ICT in Environmental Sciences: A citation analysis 1990-2007. Environmental Modelling & Software 24 (7), 865-871. DOI:10.1016/j.envsoft.2008.11.013 [3] Stallman, R. M., 2005. Free community science and the free development of science. PLoS Med 2 (2), e47+. DOI:10.1371/journal.pmed.0020047 [4] Waldrop, M. M., 2008. Science 2.0. Scientific American 298 (5), 68-73. DOI:10.1038/scientificamerican0508-68 [5] Heineke, H. J., Eckelmann, W., Thomasson, A. J., Jones, R. J. A., Montanarella, L., and Buckley, B., 1998. Land Information Systems: Developments for planning the sustainable use of land resources. 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Abstr. 13, 3351 [13] Bollinne, A., Laurant, A., and Boon, W., 1979. L’érosivité des précipitations a Florennes. Révision de la carte des isohyétes et de la carte d’erosivite de la Belgique. Bulletin de la Société géographique de Liége 15, 77-99 [14] Ferro, V., Porto, P and Yu, B., 1999. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia. Hydrolog. Sci. J. 44 (1), 3-24. DOI:10.1080/02626669909492199 [15] de Santos Loureiro, N. S. and de Azevedo Coutinho, M., 2001. A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal. J. Hydrol. 250, 12-18. DOI:10.1016/S0022-1694(01)00387-0 [16] Rogler, H., and Schwertmann, U., 1981. Erosivität der Niederschläge und Isoerodentkarte von Bayern (Rainfall erosivity and isoerodent map of Bavaria). Zeitschrift fur Kulturtechnik und Flurbereinigung 22, 99-112 [17] Nearing, M. A., 1997. 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DOI:10.1145/358896.358899 [24] Quarteroni, A., Saleri, F., 2006. Scientific Computing with MATLAB and Octave. Texts in Computational Science and Engineering. Milan, Springer-Verlag [25] The MathWorks, 2011. MATLAB. http://www.mathworks.com/help/techdoc/ref/ [26] Eaton, J. W., Bateman, D., and Hauberg, S., 2008. GNU Octave Manual Version 3. A high-level interactive language for numerical computations. Network Theory Limited, ISBN: 0-9546120-6-X [27] de Rigo, D., 2011. Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modeling. The Mastrave project. http://mastrave.org/doc/MTV-1.012-1 [28] de Rigo, D., (exp.) 2012. Semantic array programming for environmental modelling: application of the Mastrave library. In prep. [29] Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility. In prep. [30] R Development Core Team, 2005. 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This dataset provides shapefile outlines of the 7,150 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 7,150 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9CA6XP8).

This appendix lists all literature citations used for data comparisons to the model output. (0.03 MB DOC)

The global hydraulic analysis software market is experiencing robust growth, driven by increasing infrastructure development, stringent water management regulations, and the rising adoption of Building Information Modeling (BIM) in the construction industry. The market's expansion is fueled by the need for efficient and accurate hydraulic modeling to optimize water distribution networks, design efficient irrigation systems, and analyze flood risks. Advancements in computational fluid dynamics (CFD) and the integration of GIS technology are further enhancing the capabilities of these software solutions, attracting a wider range of users across various sectors, including water utilities, engineering firms, and government agencies. While the market currently enjoys significant growth, challenges remain in terms of the high initial investment required for software licenses and the need for specialized training to effectively utilize complex software features. Nevertheless, the long-term benefits of improved efficiency, reduced operational costs, and enhanced decision-making outweigh these initial hurdles, driving sustained market expansion. The competitive landscape is characterized by a mix of established players like Bentley Systems and open-source options. This dynamic environment fosters innovation and the development of specialized software tailored to specific needs, such as analysis of open channel flows or pipe networks. Future growth is likely to be driven by the incorporation of Artificial Intelligence (AI) and Machine Learning (ML) for predictive modeling and improved automation. The integration of cloud-based solutions will also play a crucial role in expanding accessibility and collaboration among stakeholders involved in hydraulic projects. Regional variations in market growth will likely reflect differences in infrastructure investment and technological adoption rates. Assuming a conservative CAGR of 12% based on industry trends, and a 2025 market size of $500 million, the market is projected to significantly expand over the forecast period (2025-2033).

Four digital water-surface profile maps for a 14-mile reach of the Mississippi River near Prairie Island in Welch, Minnesota from the confluence of the St. Croix River at Prescott, Wisconsin to upstream of the United States Army Corps of Engineers (USACE) Lock and Dam No. 3 in Welch, Minnesota, were created by the U.S. Geological Survey (USGS) in cooperation with the Prairie Island Indian Community. The water-surface profile maps depict estimates of the areal extent and depth of inundation corresponding to selected water levels (stages) at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). Current conditions for estimating near-real-time areas of water inundation by use of USGS streamgage information may be obtained on the internet at http://waterdata.usgs.gov/. Water-surface profiles were computed for the stream reach using HEC-GeoRAS software by means of a one-dimensional step-backwater HEC-RAS hydraulic model using the steady-state flow computation option. The hydraulic model used in this study was previously created by the USACE . The original hydraulic model previously created extended beyond the 14-mile reach used in this study. After obtaining the hydraulic model from USACE, the HEC-RAS model was calibrated by using the most current stage-discharge relations at the USGS streamgage Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The hydraulic model was then used to determine four water-surface profiles for flood stages referenced to 37.00, 39.00, 40.00, and 41.00-feet of stage at the USGS streamgage on the Mississippi River at Prescott, Wisconsin (USGS station number 05344500). The simulated water-surface profiles were then combined with a digital elevation model (DEM, derived from light detection and ranging (LiDAR) in Geographic Information System (GIS) data having a 0.35-foot vertical and 1.97-foot root mean square error horizontal resolution) in order to delineate the area inundated at each stage. The calibrated hydraulic model used to produce digital water-surface profile maps near Prairie Island, as part of the associated report, is documented in the U.S. Geological Survey Scientific Investigations Report 2021-5018 (https://doi.org/10.3133/ sir20215018). The data provided in this data release contains three zip files: 1) MissRiverPI_DepthGrids.zip, 2) MissRiverPI_InundationLayers.zip, and 3) ModelArchive.zip. The MissRiverPI_DepthGrids.zip and MissRiverPI_InundationLayers.zip files contain model output water-surface profile maps as shapefiles (.shp) and Keyhole Markup Language files (.kmz) that can be opened using Esri GIS systems (.shp files) or Google Earth (.kmz files), while the ModelArchive.zip contains model inputs, outputs, and calibration data used in creating the water-surface profiles maps.

The Digital Geologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sahi_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sahi_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sahi_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sahi_geology_metadata_faq.pdf). Please read the sahi_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sahi_geology_metadata.txt or sahi_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).