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.

Digital Elevation Models (DEM) are widely used to derive information for the modeling of hydrologic processes. The basic model for hydrologic terrain analysis involving hydrologic conditioning, determination of flow field (flow directions) and derivation of hydrologic derivatives is available in multiple software packages and GIS systems. However as areas of interest for terrain analysis have increased and DEM resolutions become finer there remain challenges related to data size, software and a platform to run it on, as well as opportunities to derive new kinds of information useful for hydrologic modeling. This presentation will illustrate new functionality associated with the TauDEM software (http://hydrology.usu.edu/taudem) and new web based deployments of TauDEM to make this capability more accessible and easier to use. Height Above Nearest Drainage (HAND) is a special case of distance down the flow field to an arbitrary target, with the target being a stream and distance measured vertically. HAND is one example of a general class of hydrologic proximity measures available in TauDEM. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in 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, information that is useful for hydraulic routing and stage-discharge rating calculations in hydrologic modeling. This presentation will describe the calculation of HAND and its use to determine hydraulic properties across the US for prediction of stage and flood inundation in each NHDPlus reach modeled by the US NOAA’s National Water Model. This presentation will also describe two web based deployments of TauDEM functionality. The first is within a Jupyter Notebook web application attached to HydroShare that provides users the ability to execute TauDEM on this cloud infrastructure without the limitations associated with desktop software installation and data/computational capacity. The second is a web based rapid watershed delineation function deployed as part of Model My Watershed (https://app.wikiwatershed.org/) that enables delineation of watersheds, based on NHDPlus gridded data anywhere in the continental US for watershed based hydrologic modeling and analysis.

Presentation for European Geophysical Union Meeting, April 2018, Vienna. Tarboton, D. G., N. Sazib, A. Castronova, Y. Liu, X. Zheng, D. Maidment, A. Aufdenkampe and S. Wang, (2018), "Hydrologic Terrain Analysis Using Web Based Tools," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-10337, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10337.pdf.

This dataset represents the region of hydrological influence (RHI) for National Wildlife Refuge System lands. RHI is defined as the area that is most likely influencing the hydrology and water quality encompassing the refuge. Many of these boundaries are merged USGS unique hydrologic units (e.g., HUC-6, 8, 10, 12) or generated using hydrologic modeling techniques. Please see the ‘Comment’ field for more information.Data Set Contact: U.S. Fish and Wildlife Service Natural Resource Program Center, GIS Team Lead, richard_easterbrook@fws.gov

When rain falls over land, a portion of it runs off into stream channels and storm water systems while the remainder infiltrates into the soil or returns to the atmosphere directly through evaporation.Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow.For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55).Dataset SummaryPhenomenon Mapped: Soil hydrologic groupUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic group is derived from the gSSURGO map unit aggregated attribute table field Hydrologic Group - Dominant Conditions (hydgrpdcd).The seven classes of hydrologic soil group followed by definitions:Group A - Group A soils consist of deep, well drained sands or gravelly sands with high infiltration and low runoff rates.Group B - Group B soils consist of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of infiltration and runoff.Group C - Group C consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of infiltration.Group D - Group D consists of soils with a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a clay pan or clay layer at or near the surface, and soils that are shallow over nearly impervious material.Group A/D - Group A/D soils naturally have a very slow infiltration rate due to a high water table but will have high infiltration and low runoff rates if drained.Group B/D - Group B/D soils naturally have a very slow infiltration rate due to a high water table but will have a moderate rate of infiltration and runoff if drained.Group C/D - Group C/D soils naturally have a very slow infiltration rate due to a high water table but will have a slow rate of infiltration if drained.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "soil hydrologic group" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "soil hydrologic group" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

GEOGLOWS is the Group on Earth Observation's Global Water Sustainability Program. It coordinates efforts from public and private entities to make application ready river data more accessible and sustainably available to underdeveloped regions. The GEOGLOWS Hydrological Model provides a retrospective and daily forecast of global river discharge at 7 million river sub-basins. The stream network is a hydrologically conditioned subset of the TDX-Hydro streams and basins data produced by the United State's National Geospatial Intelligence Agency. The daily forecast provides 3 hourly average discharge in a 51 member ensemble and 15 day lead time derived from the ECMWF Integrated Forecast System (IFS). The retrospective simulation is derived from ERA5 climate reanalysis data and provides daily average streamflow beginning on 1 January 1940. New forecasts are uploaded daily and the retrospective simulation is updated weekly on Sundays to keep the lag time between 5 and 12 days.

The geoglows-v2 bucket contains: (1) model configuration files used to generate the simulations, (2) the GIS streams datasets used by the model, (3) the GIS streams datasets optimized for visualizations used by Esri's Living Atlas layer, (4) several supporting table of metadata including country names, river names, hydrological properties used for modeling.

The geoglows-v2-forecasts bucket contains: (1) daily 15 forecasts in zarr format optimized for time series queries of all ensemble members in the prediction, (2) CSV formatted summary files optimized for producing time series animated web maps for the entire global streams dataset.

The geoglows-v2-retrospective bucket contains: (1) the model retrospective outputs in (1a) zarr format optimized for time series queries of up to a few hundred rivers on demand as well as (1b) in netCDF format best for bulk downloading the dataset, (2) estimated return period flows for all 7 million million rivers (2a) in zarr format optimized for reading subsets of the dataset as well as (2b) in netCDF format best for bulk downloading. (3) The initialization files produced at the end of each incremental simulation useful for restarting the model from a specific date.

This Model Instance provides a file structure and R scripts for estimating trends in suspended sediment concentration and investigating those trends with a record of disturbances to determine correlative relationships, as described in K.M. Humphreys and D.C. Mays (2025) Evaluating Trends and Insights from Historical Suspended Sediment and Land Management Data in the South Fork Clearwater River Basin, Idaho County, Idaho, USA, Hydrology (Volume 12).

The global hydrological software market is estimated to be valued at USD 733 million in 2025 and is projected to grow at a CAGR of 8.1% from 2025 to 2033. The increasing demand for water resource management, coupled with the growing need for accurate and reliable hydrological data, is driving the growth of the market. The rising adoption of cloud-based software and the increasing use of hydrological models in various applications are further contributing to the market growth. North America and Europe are expected to remain the key markets for hydrological software, followed by Asia-Pacific and the Middle East & Africa. Key market trends include the increasing integration of hydrological software with other software systems, such as geographic information systems (GIS) and water resource planning models. The growing use of big data and machine learning in hydrological modeling is also expected to drive the market growth. The market is characterized by the presence of several major vendors, such as GARDENIA, GeoHECHMS, GEOSTRU, Green Kenue, HydroCAD, HydroLogic, Hydrology Studio, HydroOffice, HydroVisE, Hysim, MIKE SHE, Raven, UKCEH, Wflow, and WMS. These vendors offer a wide range of software solutions for various hydrological applications. The market is highly competitive, with vendors focusing on innovation and differentiation to gain market share. Hydrological Software: Global Market Size Market Size: USD US$ 687.9 Million in 2023; expected to reach USD 1,153.7 Million by 2033 Historical CAGR: 6.1% from 2018 to 2023 Forecast CAGR: 6.7% from 2023 to 2033 Web Links:

GARDENIA GeoHECHMS GEOSTRU Green Kenue HydroCAD HydroLogic Hydrology Studio HydroOffice HydroVisE Hysim MIKE SHE Raven UKCEH Wflow WMS

This resource contains data inputs and an iPython Jupyter Notebook used to simulate semi-distributed variable source area runoff generation in a tributary to the Logan River. This resource is part of the 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) calculate the topographic wetness index using digital elevation models (DEMs) following up on a previous module on DEMs and GIS in Hydrology; (2) apply TOPMODEL concepts and equations to estimate soil moisture deficit and runoff generation across a watershed given necessary watershed and storm characteristics; and (3) critically assess concepts and assumptions to determine if and why TOPMODEL is an appropriate tool given information about a specific watershed.

Please note that this exercise sets up the data needed to estimate runoff in the Spawn Creek watershed using TOPMODEL. Spawn Creek is a tributary of the Logan River, Utah. This exercise uses some of the same data as the Logan River Exercise in Digital Elevation Models and GIS in Hydrology at https://www.hydroshare.org/resource/9c4a6e2090924d97955a197fea67fd72/. If running the TOPMODEL for other study sites, you need to prepare a DEM TIF file and an outlet shapefile for the area of interest. - 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.

The visible centerline of a hydrology feature within ortho imagery taken in 2006 that is less than 10' wide. Additionally, centerline abstractions oriented in the direction of flow through all open water bodies captured in the hydrology_waterbody feature class. The resulting lines are designed to create a geographic network compatible with the National Hydrological Dataset (NHD) modeling standards for representing NHD drainage network. This data set is compiled at 1" = 100' and is designed to serve as Kankakee County's GIS base map.

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. Office for Official Publications of the European Communities, Luxembourg. EUR 17729 EN [6] Farr, T. G., Rosen, P A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D., 2007. The Shuttle Radar Topography Mission. Review of Geophysics 45, RG2004, DOI:10.1029/2005RG000183 [7] Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M., 2008. A European daily high-resolution gridded dataset of surface temperature and precipitation. Journal of Geophysical Research 113, (D20) D20119+ DOI:10.1029/2008jd010201 [8] Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D. C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture handbook 703. US Dept Agric., Agr. Handbook, 703 [9] Bosco, C., Rusco, E., Montanarella, L., Panagos, P., 2009. Soil erosion in the alpine area: risk assessment and climate change. Studi Trentini di scienze naturali 85, 119-125 [10] Bosco, C., Rusco, E., Montanarella, L., Oliveri, S., 2008. Soil erosion risk assessment in the alpine area according to the IPCC scenarios. 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. 47-58 [11] de Rigo, D. and Bosco, C., 2011. Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment. IFIP Advances in Information and Communication Technology 359 (34), 310-31. DOI:10.1007/978-3-642-22285-6_34 [12] Bosco, C., de Rigo, D., Dewitte, O., and Montanarella, L., 2011. Towards a Reproducible Pan-European Soil Erosion Risk Assessment - RUSLE. Geophys. Res. 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. A single, continuous function for slope steepness influence on soil loss. Soil Sci. Soc. Am. J. 61 (3), 917-919. DOI:10.2136/sssaj1997.03615995006100030029x [18] Morgan, R. P C., 2005. Soil Erosion and Conservation, 3rd ed. Blackwell Publ., Oxford, pp. 304 [19] Šúri, M., Cebecauer, T., Hofierka, J., Fulajtár, E., 2002. Erosion Assessment of Slovakia at regional scale using GIS. Ecology 21 (4), 404-422 [20] Cebecauer, T. and Hofierka, J., 2008. The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology 98, 187-198. DOI:10.1016/j.geomorph.2006.12.035 [21] Poesen, J., Torri, D., and Bunte, K., 1994. Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23, 141-166. DOI:10.1016/0341-8162(94)90058-2 [22] Wischmeier, W. H., 1959. A rainfall erosion index for a universal Soil-Loss Equation. Soil Sci. Soc. Amer. Proc. 23, 246-249 [23] Iverson, K. E., 1980. Notation as a tool of thought. Commun. ACM 23 (8), 444-465. 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. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [31] Stallman, R. M., 2009. Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52 (6), 31–33. DOI:10.1145/1516046.1516058 [32] de Rigo, D. 2011. Multi-dimensional weighted median: the module "wmedian" of the Mastrave modelling library. Mastrave project technical report. http://mastrave.org/doc/mtv_m/wmedian [33] Shakesby, R. A., 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Reviews 105 (3-4), 71-100. DOI:10.1016/j.earscirev.2011.01.001 [34] Zuazo, V. H., Pleguezuelo, C. R., 2009. Soil-Erosion and runoff prevention by plant covers: A review. In: Lichtfouse, E., Navarrete, M., Debaeke, P Véronique, S., Alberola, C. (Eds.), Sustainable Agriculture. Springer Netherlands, pp. 785-811. DOI:10.1007/978-90-481-2666-8_48

This linear dataset represents surface water feature extents that cumulatively drain in excess of 125 hectares upstream. The 125 hectare threshold is an approximation that was derived using traditional GIS hydrology modeling tools from a flow accumulation raster grid based off of the provincial elevation model. The resulting reaches are a guide for determining potential riverine flood hazards based on provincial recommendations for floodplain mapping requirements in application to the Authority’s regulation.

As per Cognitive Market Research's latest published report, the Global Hydraulics and Hydrology Software market size will be $940.56 Million by 2028. Hydraulics and Hydrology Software Industry's Compound Annual Growth Rate will be 7.48% from 2023 to 2030.

The North America Hydraulics and Hydrology Software market size will be USD 306.25 Million by 2028.

What is Driving Hydraulics and Hydrology Software Industry Growth?

Need for water distribution

Living organisms need water to survive. Water is present in abundant quantities on and under Earth’s surface, but less than 1 percent of it is liquid fresh water. Although approximately 98 percent of liquid fresh water exists as groundwater, much of it occurs very deep. Thus to execute uniform supply of water, it is necessary to store, treat and distribute. It is extremely necessary to treat water as it has a strong tendency to dissolve other substances, so it is rarely found in its pure form.

People wholly depends on water for drinking, cooking, washing, carrying away wastes, and other domestic needs. Water supply systems must also meet requirements for public, commercial, and industrial activities. In all cases, the water must fulfil both quality and quantity requirements. Further, development of infrastructure due to rise in urbanization and population, requirement for water distribution is increasing.

According to the latest joint report of the WHO and UNICEF, over 884 million people have no access to improved drinking water. Delivering domestic water to the point of consumption requires some degree of engineering. Hydraulics and hydrology software provides comprehensive and easy decision-support tool for water distribution networks. The software helps in generating master plans, support land development projects, and optimize the operations of water distribution, wastewater, and storm water systems.

Thus, need for water distribution drives the growth of hydraulics and hydrology software market.

Restraints for Hydraulics and Hydrology Software Market

High initial cost associated with the installation.(Access Detailed Analysis in the Full Report Version)

Opportunities for Hydraulics and Hydrology Software Market

Rising concerns regarding waste-water treatment.(Access Detailed Analysis in the Full Report Version)

Introduction of Hydraulics and Hydrology Software

Hydrology and Hydraulics Software is used to create master plans, assist land development projects, and improve water distribution, wastewater, and storm water system operations. The programme helps any water utility to achieve its operational and managerial objectives, such as energy efficiency, design cost, and resource management.

Hydraulic and hydrological modelling are essential tools for analyzing network behavior, planning and designing changes to water infrastructure systems, and predicting water cycle activities. The programme includes tools for automating numerous delineations, computations, and modelling operations, both simple and complicated. It assists in increasing capacity to provide appropriate service levels while also allowing for additional flexibility.

Furthermore, the software is used by water professionals at utilities and engineering firms to plan intelligently and deliver clean water safely, accurately model water system operations, make reliable renewal decisions, reduce emergency response time, deliver high-quality design projects with minimal capital investments, and improve team productivity with sustainable GIS and CAD-integrated hydraulic models.

Hydraulic and hydrological software is used in water, storm water, wastewater, and other applications and is accessible on premise and in the cloud. Hydraulic and hydrological software is commonly used in sectors such as water and wastewater treatment, water distribution systems, oil and gas, building and architecture, and many more.

Many nations are currently seeing an increase in storms and floods, necessitating the development of effective hydraulics and hydrological models. Henceforth, industry participants are implementing technologies like Artificial Intelligence to make the process easier. As a result, the market for hydraulics and hydrology software is growing.

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/.

Nile Delta Palimpsestual-river-network (PRN) CC-BY-SA-4.0 license

The data offered here was created as part of the PhD project Hinojosa-Baliño, I. (2022). Urban fluctuations in the north-central region of the Nile Delta: 4000 years of river and urban development in Egypt. [Doctoral, Durham University]. The these can be downloaded from Durham eTheses.

Data files are offered as Geopackages (GPKG) and the main files include Metadata in QMD format, but they should have their own Metadata embedded.

Contents

• PRN (realistic). Palimpsestual river network with channel width represented with a realistic width based on different sources.
• Spider network combined with PRN (lines). Palimpsestual reconstructed river network with approximate channel width, type of channel and potential speed that a boat could have achieved in that specific channel.
• Lagoons. This is a polygon layer representing the boundaries of lakes in the Nile Delta. This is just a background layer.

When rain falls over land a portion of it runs off into stream channels and storm water systems while the remainder is absorbed into the soil or returns to the atmosphere directly through evaporation.Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow.For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55).Dataset SummaryThis layer provides access to an image service with a cell size of 30 meters. It is derived from the 2014 version of the gSSURGO 30m raster (contiguous 48 States and Washington D.C.) and 10m raster (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic soil group was derived from the gSSURGO Map Unit Aggregate Attribute table field Hydrologic Group - Dominant Conditions (hydgrpdcd).This 30m resolution layer covers most of the continental United States, portions of Alaska, and Hawaii, Puerto Rico, the U.S. Virgin Islands, and several Pacific Islands including Guam and Saipan. The layer was created from the 2014 SSURGO snapshot.The seven classes of hydrologic soil group are:Group A soils have a high infiltration rate and low runoff. These soils consist of deep, well drained sands or gravelly sands and have a high rate of water transmission.Group B soils have a moderate infiltration rate. This group consists chiefly of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of water transmission.Group C soils have a slow infiltration rate. This group consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of water transmission.Group D soils have a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a claypan or clay layer at or near the surface, and soils that are shallow over nearly impervious material. These soils have a very slow rate of water transmission.If a soil is placed in group D because of a high water table it may be assigned to a dual hydrologic group: A/D, B/D, or C/D. The first letter of the pair represents the soil’s group if drained and the D represents the natural condition.For more information on soil hydrologic groups see the Natural Resources Conservation Service's National Engineering Handbook.The original gSSURGO dataset is available from the NRCS’s Geospatial Data Gateway.Link to source metadata

The global stormwater design software market is experiencing robust growth, driven by increasing urbanization, stricter environmental regulations, and the growing need for efficient water management solutions. The market, estimated at $1.5 billion in 2025, is projected to expand at a compound annual growth rate (CAGR) of 8% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the escalating frequency and intensity of extreme weather events, including flash floods and urban inundation, are compelling municipalities and infrastructure developers to adopt advanced stormwater management strategies. Secondly, governments worldwide are implementing stringent regulations to minimize the environmental impact of stormwater runoff, fostering demand for software solutions that aid in compliance. Finally, the increasing adoption of cloud-based technologies is enabling greater accessibility and collaboration among stakeholders involved in stormwater projects, further boosting market growth. The cloud-based segment is expected to dominate the market due to its scalability, cost-effectiveness, and real-time data analysis capabilities. Key applications include urban planning, water conservancy projects, and agriculture, with significant potential for growth in developing nations with rapidly expanding urban areas and agricultural sectors. Competitive landscape analysis reveals a mix of established players like Autodesk and emerging technology providers, leading to innovation and improved software functionality. Market restraints include the high initial investment cost associated with software implementation and the requirement for specialized expertise in its operation. However, the long-term benefits in terms of cost savings, improved efficiency, and environmental protection are expected to outweigh these initial challenges. The market segmentation reveals significant growth potential across various geographic regions. North America and Europe currently hold the largest market share, driven by well-established infrastructure and advanced technological adoption. However, rapid urbanization and infrastructure development in Asia-Pacific, particularly in China and India, are expected to fuel substantial market growth in the coming years. The increasing availability of affordable and accessible technology is also expanding the market penetration in emerging economies. Moreover, the integration of Geographic Information Systems (GIS) and Building Information Modeling (BIM) technologies within stormwater design software is enhancing its capabilities and broadening its application across diverse sectors. This convergence is leading to the development of holistic and sustainable water management solutions, contributing to the market's overall growth trajectory.

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

The global market for Detention Pond Analysis and Design Software is experiencing robust growth, driven by increasing urbanization, stricter environmental regulations, and the need for efficient stormwater management solutions. The market size in 2025 is estimated at $250 million, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising frequency and intensity of extreme weather events necessitate sophisticated software solutions for accurate detention pond design and analysis. Secondly, government mandates and incentives for sustainable infrastructure development are pushing adoption across both commercial and government sectors. Thirdly, advancements in software capabilities, including integration with GIS data, hydraulic modeling enhancements, and cloud-based accessibility, are making these tools more efficient and user-friendly. The software segment is expected to be the largest contributor to market revenue due to its scalability and ease of integration into existing workflows. However, the market also faces some challenges. High initial investment costs for software licenses and training can hinder adoption, particularly among smaller firms. Additionally, the complexity of hydrological modeling and the need for specialized expertise can limit widespread use. Despite these restraints, the long-term outlook remains positive, with continuous innovation and increasing awareness of the importance of effective stormwater management expected to drive further market expansion. The North American region is projected to hold the largest market share initially, due to strong regulatory frameworks and significant investment in infrastructure projects. However, Asia-Pacific is poised for rapid growth over the forecast period driven by expanding urbanization and infrastructure development initiatives in countries such as China and India. Key players in this market include Bentley Systems, CULTEC, Innovyze, HydroCAD, MWH, IBM, Computational Hydraulics International (CHI), and Hydrology Studio, each contributing to a competitive yet innovative market landscape.

The statistically-based estimates of streamflow included here are for the headwater watersheds in the study area described in LaFontaine and others (2019), and were developed using the ordinary kriging methodology described in Farmer (2016). There are four files included that describe the maximum, minimum, mean, and median estimated streamflow for each headwater on a daily time step for the period 10/1/1980-9/30/2010. A GIS shapefile of the headwaters is also included here. Farmer, W.H., 2016, Ordinary kriging as a tool to estimate historical daily streamflow records: Hydrology and Earth System Sciences, v. 20, no. 7, p. 2721-2735, accessed September 27, 2017, at https://doi.org/10.5194/hess-20-2721-2016. LaFontaine, J.H., Hart, R.M., Hay, L.E., Farmer, W.H., Bock, A.R., Viger, R.J., Markstrom, S.L., Regan, R.S., and Driscoll, J.M., in review, Simulation of Water Availability in the Southeastern United States for Historical and Potential Future Climate and Land-Cover Conditions: U ...

The stream segments available here were extracted from a previous application of the Precipitation Runoff Modeling System (PRMS) in the Apalachicola-Chattahoochee-Flint River Basin by LaFontaine and others (2013). A Geographic Information System (GIS) file for the stream segments is provided as a shapefile with attribute seg_id identifying the numbering convention used in the PRMS models of the upper Chattahoochee River Basin in northeast Georgia. These GIS files represent the watershed area upstream of U.S. Geological Survey streamgage 02335000.