ABSTRACT:

These are the my visited countries

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VIEWBUILD

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Countries layers that contains all teh countries in the world

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Streamme is a collection for the stream3d

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The Aqueduct Global Flood Risk Maps provide current and future river flood risk estimates in urban damage, affected GDP, and affected population by country, river basin, and state.

The datasets in these maps include current and future river flood risk estimates in urban damage, affected GDP, and affected population by country, river basin, and state.

For the current scenario, we used hydrological data from 1960 through 1999 for generating flood inundations for 9 return periods, from 2-year flood to 1000-year flood, and 2010 GDP, population, and land use data for assessing flood impacts.

For future projections, we used 5 GCMs (Global Climate Models) from CMIP5 (Coupled Model Intercomparison Project Phase 5) projecting future flood inundations under two climate scenarios, RCP4.5 (Representative Concentration Pathway) and RCP8.5, and projected socio-economic changes using SSP2 (Shared Socio-economic Pathway) and SSP3, from the Intergovernmental Panel on Climate Change Assessment Report 5.

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Population and other GDP data

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This resource contains the boundary for the La Plata Basin for the Hydro Explorer Tethys Application

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These are the provinces of BOlivia

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Resource for the INDRI hydroviewer for the model MOOD

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fafafasf

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Contains the Castanuelas station discharge data as a excel file from Dominican Republic,and it also contains a nice picture from the Bolivian Carnival. It also containsboundaries shapefiles for the different boundaries of the Dominican Republic,a DEM, and the stream network of the country with its major roads. In addition, it also contains the Boudnary of Haiti in order to separate the two boudnaries in the island

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Silvia affected areas shapefile

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3491809221
8162412134
2458140625
9254527084
1595649514
4889027516
9505035741
4047558622
0471843261
0667132419
7340964358
6047310174
0657897392
6368164623
8099217974
8361710176
3855788725
2221083966
4894641901
6907341616
3788634134
4627011048
3831685360
7466953106
0722872811
4265985254
0108530825
6453615613
8397942694
1220362913
0316839644
7082358003
5004841632
8879516995
4882532389
7830791062
1489854541
8180204108
1504447793
6283227609
4146141191
0112213773
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8705187575
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6558110201
5583128571
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3293454577
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1146795304
3480206700
5823188813
5034373030
4834188224
3868432019
0950526178
7286722182
8311538136
7329994514
7530949973
7728994450
4172795655
0024965576

ABSTRACT:

Reaches hydroshare Region created using the OWP Hydroviewer

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Reaches geometry region Region created using the OWP Hydroviewer

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reaches Region created using the OWP Hydroviewer

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demo 14 Region created using the OWP Hydroviewer

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test 1 rt Region created using the OWP Hydroviewer

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test 2 rt Region created using the OWP Hydroviewer

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test 3 rt Region created using the OWP Hydroviewer

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test 3 pt Region created using the OWP Hydroviewer

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test 4 pt Region created using the OWP Hydroviewer
image_url: https://picsum.photos/200
github_url: https://github.com/Aquaveo/OWP
website_url: https://github.com/Aquaveo/OWP
documentation: https://github.com/Aquaveo/OWP

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test abstract

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test 4 rt Region created using the OWP Hydroviewer

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Test 8 pt Region created using the OWP Hydroviewer

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Test 9 pt Region created using the OWP Hydroviewer

ABSTRACT:

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

ABSTRACT:

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

ABSTRACT:

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

ABSTRACT:

test abstract

ABSTRACT:

test abstract

ABSTRACT:

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

ABSTRACT:

Este mapa presenta la distribución y características de las tres grandes cuencas hidrográficas y sus principales afluentes, que se encargan de modelar el paisaje: la cuenca amazónica, la cuenca del río de La Plata y la cuenca del Altiplano. Elaborado por la Unidad de Ordenamiento Territorial del Ministerio de Planificación del Desarrollo, en el año 2002 a escala 1:1000000

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test 4 refactor reaches Region created using the OWP Hydroviewer

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test 6 refactor reaches Region created using the OWP Hydroviewer

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test 1 refactor geometry Region created using the OWP Hydroviewer

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r1_geom_test Region created using the OWP Hydroviewer

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r2_reaches_test Region created using the OWP Hydroviewer

ABSTRACT:

test for the catchment files for nexus

ABSTRACT:

Flash flooding is a hazard in many parts of the world. In south Louisiana, the terrain is particularly flat with minimal natural slope to support efficient drainage. Flash flooding is common in urban areas where runoff from rainfall quickly accumulates in coulees and cannot drain fast enough. A flash flood is a sudden and rapid rise in stream water depth resulting from heavy, localized rainfall. With a growing population, developing neighborhoods in terrain susceptible to flash floods has become more common and therefore increased the potential for damage to private property and public infrastructure.

Hydrologists are frequently required to design flood protection infrastructure to protect people and property from the impacts of flash flooding. An important hydrologic question in designing for flood protection is: How much streamflow occurs in a river in response to a given amount of rainfall? To answer this question we need to know where water goes when it rains, how long does water reside in a watershed, and what pathway does water take to the stream channel. This module addresses these questions and also looks at how much runoff is generated from surface water input comprised of rainfall.

ABSTRACT:

This is my abstract to create a hydroshare api

ABSTRACT:

A powerful dashboard designed to visualize and analyze hydrological data using the National Water Model (NWM) services. At its core, the application integrates an interactive map, time series plots, and statistical summaries to provide comprehensive insights for water resource management.The map component allows users to visualize geographical data, including river reaches and gauge locations, providing a spatial context for hydrological phenomena. The time series feature displays water flow data for each reach and gauge over time, enabling trend analysis and prediction. Meanwhile, the stats section aggregates data from various NWM services, offering key metrics such as flow forecasts, streamflow, and runoff predictions.This application is ideal for professionals in hydrology, researchers, and decision-makers who need a unified tool to assess and predict water conditions based on real-time and historical data.

ABSTRACT:

The Flood Inundation Mapping (FIM) Visualization Deck is a web-based application designed to display and compare flood extent and depth information across various temporal and scenario conditions. It provides a front-end interface for accessing geospatial flood data and interacting with mapped outputs generated from hydraulic modeling.

Core Functions:
• Flood Extent Mapping: Visualizes flood extents from modeled scenarios (e.g., 2-year, 10-year, 100-year events) and real-time conditions based on streamflow observations or forecasts.
• Flood Depth Visualization: Displays depth rasters over affected areas, derived from hydraulic simulations (e.g., HEC-RAS).
• Scenario Comparison: Allows side-by-side viewing of multiple FIM outputs to support calibration or decision analysis.
• Layer Management Toolbox: Users can toggle basemaps, adjust layer transparency, load datasets, and control map extents.

Data Inputs:
• Precomputed flood inundation extents (raster/tile layers)
• Depth grids
• Stream gauge metadata
• Associated hydraulic model outputs

Technical Stack:
• Front-end: Built with JavaScript, primarily using Leaflet.js for interactive map rendering.
• Back-end Services: Uses GeoServer to serve raster tiles and vector layers (via WMS/WFS). Uses OGC-compliant services and REST endpoints for data queries.
• Data Formats: Raster layers (e.g., GeoTIFF, PNG tiles), vector layers (GeoJSON, shapefiles), elevation models, and model-derived grid outputs.
• Database: Integrates with a PostgreSQL/PostGIS backend or similar spatial database for hydrologic and geospatial data management.
• Deployment: Hosted via University of Iowa infrastructure, with modular UI elements tied to specific watersheds or study areas.

Intended Use:
The application provides a reference and exploratory tool for comparing modeled flood scenarios, visualizing extent and depth data, and interacting with region-specific inundation data products.

ABSTRACT:

A powerful dashboard designed to visualize and analyze hydrological data using the National Water Model (NWM) services. At its core, the application integrates an interactive map, time series plots, and statistical summaries to provide comprehensive insights for water resource management.The map component allows users to visualize geographical data, including river reaches and gauge locations, providing a spatial context for hydrological phenomena. The time series feature displays water flow data for each reach and gauge over time, enabling trend analysis and prediction. Meanwhile, the stats section aggregates data from various NWM services, offering key metrics such as flow forecasts, streamflow, and runoff predictions.This application is ideal for professionals in hydrology, researchers, and decision-makers who need a unified tool to assess and predict water conditions based on real-time and historical data. The dashboard features a high-resolution, layered map interface powered by NWM data, enabling users to overlay critical hydrological features such as river networks, streamflow pathways, USGS gauge stations, and flood risk zones. Users can toggle between real-time data (e.g., current streamflow, precipitation anomalies) and forecast models (e.g., 7-day flood projections) while leveraging spatial query tools to isolate specific watersheds or regions. Customizable basemaps (topographic, satellite, or hydrographic) and clickable features provide instant access to metadata, such as reach identifiers, elevation profiles, and historical flood events. Integration with geospatial APIs allows for cross-referencing with external datasets, such as land use patterns or infrastructure vulnerabilities, enhancing situational awareness for disaster preparedness.

ABSTRACT:

The World Hydrological Observing System (WHOS), operating under the World Meteorological Organization (WMO) Data Policy, serves as a global gateway for the standardized exchange of hydrological, meteorological, and climate-related environmental data. Designed to uphold principles of open access and transparency, WHOS eliminates the need for centralized data storage by dynamically linking users to original data providers—such as national hydrometeorological agencies, research institutions, and monitoring networks—through its advanced Discovery and Access Broker (DAB) technology. This middleware framework harmonizes disparate data formats and protocols (e.g., OGC WaterML 2.0, ISO metadata standards), enabling seamless interoperability across geographic and institutional boundaries. Users gain real-time access to critical datasets, including river discharge, groundwater levels, and precipitation trends, while adhering to strict Terms of Use that prohibit unauthorized commercial exploitation, mandate attribution to source agencies in publications or downstream services, and require acknowledgment of inherent risks (e.g., data latency, sensor inaccuracies).

The WMO explicitly disclaims liability for decisions or damages arising from data use, emphasizing user responsibility to verify data quality and applicability. Terms are subject to change, potentially altering access permissions or usage rights, necessitating regular policy reviews by stakeholders. By prioritizing decentralized governance and FAIR (Findable, Accessible, Interoperable, Reusable) data principles, WHOS empowers global collaboration in addressing water-related challenges, from transboundary basin management to climate adaptation strategies, while safeguarding data sovereignty and intellectual property rights of contributing entities.

ABSTRACT:

The World Hydrological Observing System (WHOS), operating under the World Meteorological Organization (WMO) Data Policy, serves as a global gateway for the standardized exchange of hydrological, meteorological, and climate-related environmental data. Designed to uphold principles of open access and transparency, WHOS eliminates the need for centralized data storage by dynamically linking users to original data providers—such as national hydrometeorological agencies, research institutions, and monitoring networks—through its advanced Discovery and Access Broker (DAB) technology. This middleware framework harmonizes disparate data formats and protocols (e.g., OGC WaterML 2.0, ISO metadata standards), enabling seamless interoperability across geographic and institutional boundaries. Users gain real-time access to critical datasets, including river discharge, groundwater levels, and precipitation trends, while adhering to strict Terms of Use that prohibit unauthorized commercial exploitation, mandate attribution to source agencies in publications or downstream services, and require acknowledgment of inherent risks (e.g., data latency, sensor inaccuracies).

This application demonstrates two projects developed by the University of Iowa Hydroinformatics Lab (UIHI Lab): HydroLang and HydroCompute. HydroLang is an open-source web framework designed for hydrology and water resources research. It offers JavaScript functions for various tasks, including retrieving and manipulating hydrologic data, performing statistical operations, generating graphical data representations, and mapping geospatial data. HydroCompute, on the other hand, is an open-source computational library geared towards hydrology and environmental sciences. It operates natively in web browsers and utilizes state-of-the-art computation standards to enable web applications to tap into the computational capabilities of the devices they run on. This includes leveraging multithreading with web workers, processing with GPUs, and running executables built in WebAssembly (WASM). This application serves as the solution for HydroCompute Case Study 3: Dashboard for Station Statistical Analysis tutorial. This tutorial was developed by the University of Iowa Hydroinformatics Lab for the 2023 CIROH Developer Conference.

ABSTRACT:

This app produces basic maps and timeseries using data from the GRACE mission.
NASA’s GRACE mission provides the first opportunity to directly measure groundwater changes from space. By observing changes in the Earth’s gravity field, scientists can estimate changes in the amount of water stored in a region, which cause changes in gravity. GRACE provides a more than 10 year-long data record for scientific analysis. This makes a huge difference for scientists and water managers who want to understand trends in how our resources are being consumed over the long term. GRACE has returned data on some of the world’s biggest aquifers and how their water storage is changing [e.g. Rodell and Famiglietti, 2001; Yeh et al., 2006; Rodell et al., 2007]. Using estimates of changes in snow and surface soil moisture, scientists can calculate an exact change in groundwater in volume over a given time period.
A study by Rodell et al. [2009] in northwest India used terrestrial water storage-change observations from GRACE and simulated soil-water variations from a data-integrating hydrological modeling system to show that groundwater is being depleted at a mean rate of 4.0 +/- 1.0 cm yr-1 equivalent height of water (17.7 +/- 4.5 km3 yr-1) over the Indian states of Rajasthan, Punjab and Haryana (including Delhi). During the study period of August 2002 to October 2008, groundwater depletion was equivalent to a net loss of 109 km3 of water, which is double the capacity of India's largest surface-water reservoir.

ABSTRACT:

This application is designed to deliver global, interactive visualizations of critical snowpack parameters—including spatial snow cover extent, snow mass (snowpack density), and snow water equivalent (SWE)—to enhance hydrological forecasting and climate resilience. By aggregating and processing geospatial snow data from satellites, ground sensors, and model outputs, the app enables users to analyze temporal and spatial trends in snow dynamics, such as melt rates, accumulation patterns, and seasonal variability. These insights directly support flood risk mitigation, drought preparedness, and water supply planning by reducing uncertainties in predicting snowmelt-driven streamflow. The platform integrates near-real-time imagery from NASA’s Global Imagery Browse Service (GIBS), a publicly accessible repository managed by the Earth Science Data and Information System (ESDIS) with funding from NASA Headquarters. GIBS provides high-resolution snow cover maps via services like MODIS and VIIRS, which are rendered as interactive, time-lapse layers within the app.

Citing the foundational work of Kadlec et al. (2016), the app employs methodologies for extracting snow cover time series from web mapping tile services, ensuring compatibility with open-access standards. Their research demonstrates how tile-based protocols can efficiently disseminate large-scale snow datasets while minimizing server load, a principle embedded in this tool’s architecture. Users, from hydrologists to emergency managers, can overlay snow metrics with terrain or watershed boundaries, export data for hydrological models (e.g., SWAT), and correlate snowpack trends with historical flood events. Acknowledgment guidelines mandate proper attribution to NASA/ESDIS and original data providers to uphold licensing and intellectual property requirements.

ABSTRACT:

The current iteration of the SWEML produces 11,000 1 km SWE inferences for select locations throughout the Western U.S. plus the Upper Colorado River Basin. There is a heavy focus on SWE inferences in the Sierra Nevada mountains, Colorado Rockies, and Wind River Range in Wyoming. The NSM pipeline assimilates nearly 700 snow telemetry (SNOTEL) and California Data Exchange Center (CDEC) sites and combines them with processed lidar-derived terrain features for the prediction of a 1 km x 1 km SWE inference in critical snowsheds. The ML pipeline retrieves all SWE observations from SNOTEL and CDEC snow monitoring locations for the date of interest and processes the SWE observations into a model-friendly data frame alongside lidar-derived terrain features, seasonality metrics, previous SWE estimates, and location. SWEML predicts SWE using a uniquely trained multilayered perceptron network model for each region and supports an interactive visualization of the SWE estimates across the western U.S.

ABSTRACT:

Given the launch of the Cooperative Institute for Research to Operations in Hydrology (CIROH) in April, 2020, CIROH scientists from 28 different academic, government, and private are working to improve the understanding of hydrologic processes, operational hydrologic forecasting techniques and workflows, community water modeling, translation of forecasts to actionable products, and use of water predictions in decision making. National-scale streamflow modeling remains a modern challenge, as changes in the underlying hydrology from land use and land cover (LULC) change, anthropogentic streamflow modification, and general process components (reach length, hydrogeophysical processes, precipitation, temperature, etc) greatly influence hydrological modeling. To benchmark model performance, characterize improvements in hydrological modeling formuations, and generate reproducible science, the team at the Alabama Water Institute (AWI) developed Community Streamflow Evaluation System (CSES) to originally characterize the water supply forecasting skill of the National Water Model v2.1 (NWM v3.0 coming soon!) in the Great Salt Lake Basin. The tool quickly scaled and with the support of the Earth Science Information Partners (ESIP), provided an opportunity to turn the novel evaluation platform into a web application. This GitHub repository contains the code to create an innovative Tethys Web Platform to share our research tools with the great research and operational hydrological community. By using CSES, researchers and practitioners can interact with model outputs and evaluate the performance of hydrological models (currently the NWM v2.1) for their region.

ABSTRACT:

HydroShare is the web-based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). Acting as a cloud-hosted repository, it allows researchers, educators, and water-resources professionals to upload, curate, publish, and permanently archive heterogeneous hydrologic data sets and computational models in many flexible formats. Each resource receives a persistent Digital Object Identifier (DOI), making it immediately citable, shareable, and discoverable through standard scholarly search engines. Beyond storage, HydroShare fosters collaboration: project members can organise resources into collections, set fine-grained privacy controls, and work together in real time within a browser-based workspace that eliminates file-exchange hassles. Integrated web applications—ranging from Python notebooks to map-centric visualisation and model-running tools—let users analyse or re-run data directly where it lives, providing a gateway to cloud computing. The platform is continually enhanced by CUAHSI’s development team under National Science Foundation awards ACI-1148453, ACI-1148090, EAR-1338606, OAC-1664018, OAC-1664061, and OAC-1664119.

ABSTRACT:

The National Weather Service (NWS) is introducing the new National Water Prediction Service (NWPS), transforming how water resources information and services are delivered, and providing a greatly improved user experience
through enhanced displays.

The NWS has hosted river observations and forecast information on the Advanced Hydrologic Prediction Service (AHPS) web page since the late 1990s. AHPS provides near real-time river data and forecasts visualized through static maps and hydrographs, probabilistic information, static Flood Inundation Maps (FIMs) at select locations, and quantitative precipitation estimates that enable decision making. Building upon these core capabilities, dynamic
and interactive hydrographs with a longer observation period, are now available via NWPS in addition to expanded mapping controls and content. NWPS includes new tools such as dynamic real-time flood inundation mapping (for an
initial 10% of the U.S. population and growing to nearly 100% by 2026), greatly expanding the amount of information for making critical neighborhood scale water decisions. NWPS delivers enhanced visualization capabilities and an Application Programming Interface (API) which enables user applications to more efficiently access water resources data by leveraging modern web services

ABSTRACT:

The Numerical Flash Flood Alert APP (NFFA APP) proudly supports the water science community across the United States by offering streamlined access to essential flood data from all USGS stations.

Accessing water data can sometimes be challenging. Our website is designed to provide quick and easy access to vital flood information for all USGS stations, helping professionals and communities stay informed and prepared. Here’s what you can find:

Action stage: This crucial threshold helps signal when it's time to start taking action in response to potential flooding.
Basin Area: Measured in square kilometers, this gives you insights into the scale of the potential flood.
Basin slope and stream length: These key geographical features provide critical information on water flow and flood potential.
Flashiness metrics: These indicators measure the variability in historical streamflow at each station, using a color-coded system: red indicates low variability, green is medium, and blue signifies high variability.
Technological Expertise
To deliver state-of-the-art solutions, we leverage a comprehensive suite of advanced IT tools:

Hydraulic & Hydrologic Modeling: We utilize sophisticated tools such as HEC-HMS for hydrology, HEC-RAS for hydraulics, and EF5 for precision in flash flood simulation. Our models are crucial for designing effective flood warning systems, enhanced by real-time satellite data to monitor and predict flood events accurately.
Water Resources Management: Our application of WEAP aids in sustainable water resources planning across sectors like agriculture, urban centers, and treatment facilities. This tool supports our commitment to developing well-informed, sustainable management strategies through detailed numerical analysis.
Pressurized Systems: Utilizing Pipe Flow Expert, we optimize systems for efficiency and sustainability, selecting the ideal pipes and pumps based on comprehensive hydrodynamic modeling.
Data Analysis Software: We employ Python and R Studio for cutting-edge data analysis, allowing us to craft sophisticated algorithmic solutions that enhance our predictive capabilities and decision-making processes.
GIS Software: Through ArcGIS and QGIS, we deliver intricate geographical mapping and spatial data analysis. These tools are integral to our comprehensive approach, offering crucial insights into the geographic dynamics of water flow and potential flood zones.

ABSTRACT:

This module will provide you with the basic knowledge to recommend if construction of a community center should proceed considering historical data from previous hurricane impacts near the proposed site. In the first section you will learn how hurricanes form, why they cause flooding and what other impacts they have in coastal communities. In the second section you will learn how to create and analyze report quality time series of hydrologic variables and how to process these time series data to identify different hydrologic processes driving flooding using a python notebook. In the third section, you will be synthesizing all the time series analysis you conducted to support your final recommendation about building the community center.

In this module you will learn about the relationships between hurricanes, groundwater and floods as you assess if it is viable to build a community center in the North Carolina Outer Banks. You will then review the drivers and impacts of hurricanes on the hydrologic system. Specifically, you will create and analyze time series in python to identify the drivers of groundwater level changes. Finally, you will predict potential flooding zones given a coastal topographic map and groundwater level data to evaluate the suitability of constructing a new coastal community center.