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added publications and fixed GIS
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‎docs/GIS-data.md

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# GIS Data
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The GEOGLOWS GIS data used in the hydrologic model is available for users to download and use for their own purposes. This dataset is referred to as hydrography, hydrofabric, or river network. It is vector data with points and lines with coordinates, not grid data, and it includes two main components:
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The GEOGLOWS GIS data used in the hydrologic model is available for users to download and use for their own purposes. This dataset is referred to as hydrography, hydrofabric, or river network. It is vector data with points and lines with coordinates, not grid data, and it includes four main components:
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- The exact **stream center lines** used in the hydrologic model.
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- The exact **catchment boundaries** used in the hydrologic model
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Each stream centerline corresponds to exactly one unique catchment boundary. The streams and catchments each have a unique 9-digit ID that identifies the catchment. This ID is the same for the stream and the corresponding catchment.
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- The exact **stream center lines** used in the hydrologic model. Each stream has a unique 9 number ID which is referred to as a reachID, link number, or stream ID. This is the file called "streams_{vpu}.gpkg".
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- The **catchment boundaries** used in the hydrologic model. There are the boundaries around each of the streamlines and represent the area connected to that streamline. It is identified using the same link number as the stream center lines. This is the file called "catchments_{vpu}.spatialite". Each stream centerline corresponds to exactly one unique catchment boundary.
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- The **connection points** used in the hydrologic model where different stream centerlines connect. Each point has the an attribute called DSLINKNO which represents the one downstream link number for each of the points. It has another attribute called USLINKNOs. This is a comma seperated list of the link numbers upstream of the nexus point. This is the file called "nexus_{vpu}.gpkg".
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- The **merged lake catchments** used in the hydrologic model to represent the locations of lakes. Stream catchments that were identified through GIS searching to be part of a lake were merged to present the lakes. Therefore, it will have a different shape than the actual lake boundary based on the shapes of the merged stream catchments. This is the file called "lakes_{vpu}.gpkg".
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## VPUs
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The GIS data is divided into 125 smaller pieces called VPUs. This makes the large quantity of data easier to manage and access. Each VPU represents one watershed (such as the Amazon River Basin or the Nile River Basin) or a combination of watersheds. The following image shows the VPU breakdown throughout the world.
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The GIS data is divided into 125 smaller pieces called VPUs (vector processing units). This makes the large quantity of data easier to manage and access. Each VPU represents one watershed (such as the Amazon River Basin or the Nile River Basin) or a combination of watersheds. The following image shows the VPU breakdown throughout the world.
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![image](vpu-boundary.png)
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The VPU boundaries are also available for download to help identify which VPU includes a user's area of interest. Then the catchments and streams are able to downloaded as an entire VPU.
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The VPU boundaries are also available for download to help identify which VPU includes a user's area of interest. The other GIS data sets should be downloaded based on the VPU of interest and are downloaded as an entire VPU.
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- **LINKNO** - A river ID number unique to the TDXHydro delineation. In TDXHydro v1, this is not globally unique. In future versions, this will be the same as geoglowsID.
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- **DSLINKNO** - The ID of the river immediately downstream of the segment represented on that row.
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- **USLINKNO*** - There will be 1 column per river segment upstream of the river on this row.
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- **DSNODEID** - The node identifier for the node at the downstream end of the river.
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- **strmOrder** - The Strahler stream order.*
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- **Length** - Geodesic length in meters of the river segment.
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- **Magnitude** - The Shreve stream magnitude.*
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- **USContArea** - The total drainage area upstream of the most upstream point (i.e., the inlet) of this segment.*
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- **DSContArea** - The total drainage area upstream of the most downstream point (i.e., the outlet) of this segment.
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- **strmDrop** - The change in elevation between the inlet and outlet of the river segment.*
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- **Slope** - The average stream slope, equal to "strmDrop / Length."
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- **StraightL** - Distance from start to end of a river in a straight line between the first and last points.*
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- **WSNO** - Watershed number.
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- **DOUTEND** - Distance to the eventual outlet from the end of the river.*
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- **DOUTSTART** - Distance to the eventual outlet from the start of the river.*
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- **DOUTMID** - Distance to the eventual outlet from the midpoint of the river.*
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- **LengthGeodesucMaters** - Geodesic length in meters of the river segment.
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V2 streams also have the following additional attributes added by the GEOGLOWS modelers:
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‎docs/index.md

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![image](img10.png)
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## Navigating the Website
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The first page on this site gives a brief overview of GEOGLOWS and its history. After that, the bulk of the training section begins. This training is broken down into 3 main sections, each having different sub sections within them. We recommend you start with the first section (Available Data) before progressing to the second section (Accessing Data). The third section (Skills and Examples) is a more advanced section designed for people looking to complete specific tasks using the GEOGLOWS data who already have a good understanding of the first two sections. As you are learning about the data, there is also a website available with more information on how to download and use the GEOGLOWS data: https://data.geoglows.org/. This is a great resource once you have a basic understanding of the data. Here is a brief overview of what you can expect to learn:
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1. **Available Data** - This section details the available datasets through GEOGLOWS. It is divided into 3 sections:
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- **a. GIS Data** - Learn about the hydrography data used in the GEOGLOWS model, including stream centerlines, catchment boundaries, and their unique identifiers, derived from high-resolution elevation products.
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- **b. Retrospective River Discharge** - Explore over 85 years of daily average streamflow data, derived from meteorological reanalysis and updated weekly, offering insights into historical river discharge.
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- **c. Forecast River Discharge** - Understand daily river flow forecasts with a 51-member ensemble providing detailed predictions at 3-hour intervals, including uncertainty ranges for better planning.
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2. **Accessing Data** - This section explains how the previously described data can be accessed and downloaded using 4 different techniques. Each section will detail a different technique:
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- **a. A Data Catalog** - This section explains the data catalog available through AWS buckets.
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- **b. A Data Service** - This section describes how to use our REST API to access GEOGLOWS data.
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- **c. A Web Map** - This section explains the Esri Living Atlas map layer that can be loaded into any GIS software (ArcGIS or QGIS).
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- **d. A Web App** - This section introduces you to the HydroViewer, which is our web application that allows for easy visualization and download of data globally.
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3. **Skills and Examples** - This section explains more advanced techniques that can be used when using the data for specific purposes.
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- **a. Accessing and Interpreting Data** - This section includes Google Colab notebooks that show you how to use the GEOGLOWS Python package to make and customize your streamflow plots.
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- **b. Bias Correction and SABER Overview** - This section gives a brief overview of bias correction and a technique called SABER that applies bias correction to other areas.
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- **c. Bias Correction for Forecast Data** - This section provides a notebook that goes through examples of bias-correcting the forecast data.
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‎docs/overview copy.md

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# What is GEOGLOWS?
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![image](image3.png)
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## Overview
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The GEOGLOWS (Global Earth Observations Global Water Sustainability)
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initiative is a collaborative effort aimed at improving global
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water sustainability through advanced hydrological forecasting
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and data analysis. Leveraging the power of Earth observations,
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numerical weather predictions, and supercomputing, GEOGLOWS
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provides actionable information through our hydrologic model, the River Forecast System (RFS). RFS provides streamflow status and
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outlook for every river worldwide. By making decades of
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historical climatological flow data and future forecasts
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easily accessible, RFS supports informed decision-making
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in water resource management, disaster risk reduction, and
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climate adaptation. This global service enables countries
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and organizations to enhance their understanding of
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water-related challenges and implement effective solutions,
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ultimately contributing to a more sustainable and
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resilient future.
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## History
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The GEOGLOWS initiative, established under the framework
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of the Group on Earth Observations (GEO), has its roots
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in a commitment to integrate Earth observation data to
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enhance global water sustainability. The journey began
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in 2014 at the GEO Plenary in Geneva, where the need for
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coordinated water data management became evident. With
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support from partners such as NASA, ECMWF, and regional
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organizations, GEOGLOWS evolved from early efforts in the
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Dominican Republic to a broader application of global
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hydrological modeling. By leveraging advanced technologies,
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including the ECMWF’s global weather forecasts and cutting-edge
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cloud computing, GEOGLOWS pioneered global streamflow
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forecasting services in the creation of the model that is now known as RFS. These services provide critical
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information to support decision-making in water management,
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helping to mitigate the impacts of floods, droughts, and
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other water-related challenges. Over time, the initiative
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has expanded its reach, integrating local and regional
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hydrological insights, and fostering collaborations
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across continents to address the complex issues of
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water scarcity and disaster preparedness.
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## Model Formulation
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The GEOGLOWS model leverages the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) alongside the ECMWF Integrated Forecast System (IFS) to conduct detailed calculations of water and energy balances within grid cells. HTESSEL simulates how land surfaces respond to atmospheric conditions, estimating critical variables such as surface and sub-surface runoff for both operational ensemble forecasts and retrospective simulations. The model employs varying spatial resolutions for its runoff files, including approximately 25 km for historical ERA-5 data, 16 km for low-resolution ensemble members, and 8 km for high-resolution forecasts. By intersecting grid lines with specific basins and applying runoff depth values, the model calculates water volumes over different time periods, which are then routed through the stream network using the Muskingum method with the RAPID algorithm. This approach provides valuable discharge data for hydrological analysis and decision-making.
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## River Forecast System Training
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To learn more about our model, RFS, continue with out training under the River Forecast System portion of this website. This training is broken down into 3 main sections, each having different sub sections within them. We recommend you start with the first section (Available Data) before progressing to the second section (Accessing Data). The third section (Skills and Examples) is a more advanced section designed for people looking to complete specific tasks using the RFS data who already have a good understanding of the first two sections. As you are learning about the data, there is also a website available with more information on how to download and use the RFS data: https://data.geoglows.org/. This is a great resource once you have a basic understanding of the data. Here is a brief overview of what you can expect to learn:
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1. **Available Data** - This section details the available datasets from RFS. It is divided into 3 sections:
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- **a. GIS Data** - Learn about the hydrography data used in the RFS, including stream centerlines, catchment boundaries, and their unique identifiers, derived from high-resolution elevation products.
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- **b. Retrospective River Discharge** - Explore over 85 years of daily average streamflow data, derived from meteorological reanalysis and updated weekly, offering insights into historical river discharge.
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- **c. Forecast River Discharge** - Understand daily river flow forecasts with a 51-member ensemble providing detailed predictions at 3-hour intervals, including uncertainty ranges for better planning.
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2. **Accessing Data** - This section explains how the previously described data can be accessed and downloaded using 4 different techniques. Each section will detail a different technique:
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- **a. A Data Catalog** - This section explains the data catalog available through AWS buckets.
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- **b. A Data Service** - This section describes how to use our REST API to access GEOGLOWS data.
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- **c. A Web Map** - This section explains the Esri Living Atlas map layer that can be loaded into any GIS software (ArcGIS or QGIS).
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- **d. A Web App** - This section introduces you to the HydroViewer, which is our web application that allows for easy visualization and download of data globally.
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3. **Skills and Examples** - This section explains more advanced techniques that can be used when using the data for specific purposes.
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- **a. Accessing and Interpreting Data** - This section includes Google Colab notebooks that show you how to use the GEOGLOWS Python package to make and customize your streamflow plots.
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- **b. Bias Correction and SABER Overview** - This section gives a brief overview of bias correction and a technique called SABER that applies bias correction to other areas.
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- **c. Bias Correction for Forecast Data** - This section provides a notebook that goes through examples of bias-correcting the forecast data.
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## Stories of Application
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GEOGLOWS has been instrumental in transforming water management
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and disaster response across the globe. From helping predict
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floods and droughts in Nepal, to improving transboundary flow
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forecasts in Bangladesh, GEOGLOWS empowers local agencies with
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the tools to make life-saving decisions. In the Dominican Republic,
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GEOGLOWS enhances capacity for hydrological challenges, including
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flood risk and irrigation management. The service has extended
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early warning lead times in Malawi and has been crucial in
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managing reservoir releases during hurricanes in Honduras.
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These real-world applications demonstrate the global impact
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of GEOGLOWS in addressing critical water-related challenges.
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For more in-depth stories of how GEOGLOWS is making a difference
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worldwide, visit [GEOGLOWS Stories](https://stories.geoglows.org/home).
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## Joining GEOGLOWS
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Are you interested in being part of the global GEOGLOWS community?
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By joining, you can stay updated with the latest developments, collaborate
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with experts, and access valuable resources related to hydrological forecasting
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and water sustainability.
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To join the GEOGLOWS network, simply sign up for our Google Group:
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[Join the group!](https://groups.google.com/g/geoglows)
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By becoming a member, you will be part of a growing community
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focused on advancing global water sustainability and streamflow forecasting.
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