fixed RFS lableing

Author: rhuber

docs/GIS-data.md

@@ -1,12 +1,12 @@
 # GIS Data
 
 
-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:
+The GEOGLOWS GIS data used in RFS 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:
 
-- 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".
-- 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.
-- 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".
-- 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".
+- The exact **stream center lines** used in RFS.  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".
+- The **catchment boundaries** used RFS. 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.
+- The **connection points** used in RFS 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".
+- The **merged lake catchments** used in RFS 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".
 
 ---
 

docs/bias-correction.md

@@ -1,6 +1,6 @@
 # Bias Correction and SABER
 
-The GEOGLOWS Hydrologic Model exhibits biases that can limit its precision, prompting the development of a bias correction approach. To correct these systematic biases at instrumented locations, we propose the Monthly Flow Duration Curve Quantile-Mapping (MFDC-QM) method. This method targets biases related to flow variability and correlation. The GEOGLOWS Hydrologic Model does not assimilate observed streamflow data into its initial calculation. However, the bias-correction technique allows for the global data to be applied locally. Local users can have more confidence in their data because they can know that their observed data is able to be used to improve the modeled data at their location.
+RFS exhibits biases that can limit its precision, prompting the development of a bias correction approach. To correct these systematic biases at instrumented locations, we propose the Monthly Flow Duration Curve Quantile-Mapping (MFDC-QM) method. This method targets biases related to flow variability and correlation. RFS does not assimilate observed streamflow data into its initial calculation. However, the bias-correction technique allows for the global data to be applied locally. Local users can have more confidence in their data because they can know that their observed data is able to be used to improve the modeled data at their location.
 
 After applying the bias correction, we observed a significant improvement in the distribution of bias and variability ratios, with a slight improvement in correlation values across the stations, resulting in more reliable simulations and improved Kling-Gupta Efficiency (KGE) metrics: bias, variability, and correlation.
 
@@ -18,7 +18,7 @@ To dive deeper into the analysis of bias correction and performance evaluation,
 
 The SABER method is a bias correction tool designed for large hydrologic models like GEOGLOWS, specifically addressing the issue of model biases in both gauged and ungauged river basins. SABER uses flow duration curves (FDC) to compare the observed discharge with the simulated values from hydrologic models, identifying and correcting biases. For ungauged locations, where direct observations are unavailable, SABER uses the scalar flow duration curve (SFDC).
 
-Unlike bias-correction, which each institution performs locally, SABER is performed by the GEOGLOWS team and is not done by the end users. We use the gauge data made available to us to perform an improvement to all the model results. This process is still in experimentation and is not currently being applied to the data accessed by the end-users. We hope for it to be applied in future versions of the GEOGLOWS Hydrologic Model.
+Unlike bias-correction, which each institution performs locally, SABER is performed by the GEOGLOWS team and is not done by the end users. We use the gauge data made available to us to perform an improvement to all the model results. This process is still in experimentation and is not currently being applied to the data accessed by the end-users. We hope for it to be applied in future versions of RFS.
 
 SABER allows the bias correction process to extend to ungauged basins by analyzing similar watershed behaviors based on spatial proximity and clustering of flow regimes. This method is particularly useful for regions where data scarcity limits traditional calibration, such as in global models like GEOGLOWS, ensuring more accurate discharge forecasts across large spatial domains.
 

docs/data-catalog.md

@@ -1,6 +1,6 @@
 # Using AWS Buckets
 
-The **GEOGLOWS Hydrological Model Version 2** allows users to download global streamflow data directly from AWS. This provides access to both retrospective simulation data and 15-day streamflow forecasts. These datasets are hosted in S3 buckets, optimized for time series analysis and bulk downloads. 
+RFS allows users to download global streamflow data directly from AWS. This provides access to both retrospective simulation data and 15-day streamflow forecasts. These datasets are hosted in S3 buckets, optimized for time series analysis and bulk downloads. 
 
 Users can easily access and analyze these data using **Python** and **Jupyter notebooks**, with detailed tutorials available.
 

docs/data-service.md

@@ -34,7 +34,7 @@ To use the API website, follow these steps:
 
 ### Accessing the API Using Python
 
-One of the easiest ways to access the API is through Python. There is a **GeoGLOWS Python package** (documented here: [GeoGLOWS API Documentation](https://geoglows.readthedocs.io/en/latest/api-documentation.html)) that contains commands for basic analysis and querying specific types of data.
+One of the easiest ways to access the API is through Python. There is a **GEOGLOWS Python package** (documented here: [GEOGLOWS API Documentation](https://geoglows.readthedocs.io/en/latest/api-documentation.html)) that contains commands for basic analysis and querying specific types of data.
 
 This Python notebook provides examples of using the API in Python, as well as utilizing the Python package: [Programmatic_Access Colab.ipynb](https://colab.research.google.com/drive/19PiUTU2noCvNGr6r-1i9cv0YMduTxATs?usp=sharing)
 

docs/exercises.md

@@ -1,10 +1,8 @@
 ## Return periods, flow duration curves, and average flows
 
-In hydrological analysis, return periods are used to estimate the probability of extreme events like floods. While the Weibull Distribution is often used to calculate return periods based on historical data, it is limited by the length of the data series and cannot predict events beyond the observed records. For the GEOGLOWS Model, the Gumbel Distribution is applied instead, as it better models extreme values and allows for extrapolation, making it possible to calculate return periods for events beyond the available data. 
+In hydrological analysis, return periods are used to estimate the probability of extreme events like floods. While the Weibull Distribution is often used to calculate return periods based on historical data, it is limited by the length of the data series and cannot predict events beyond the observed records. For RFS, the Gumbel Distribution is applied instead, as it better models extreme values and allows for extrapolation, making it possible to calculate return periods for events beyond the available data. 
 
-Additionally, **Flow Duration Curves (FDCs)** are used to represent the percentage of time that streamflow is likely to equal or exceed certain flow rates, providing insights into the variability of water resources. The model also includes the analysis of daily seasonality to understand patterns of streamflow throughout the year, monthly seasonality to observe changes between months, and annual mean discharge to detect long-term trends. These analyses are critical for effective water resource management, flood forecasting, and understanding hydrological patterns. The following presentation gives more of a background on the return periods and flow duration curves. It shows the equations used in the GEOGLOWS Hydrologic Model to represent these things.
-
-[Return_Periods-FDC-Average_Flows.pdf](https://drive.google.com/file/d/10Si933D0fxaUrJFmIJr-WdOyjOkm453m/view?usp=sharing)
+Additionally, **Flow Duration Curves (FDCs)** are used to represent the percentage of time that streamflow is likely to equal or exceed certain flow rates, providing insights into the variability of water resources. The model also includes the analysis of daily seasonality to understand patterns of streamflow throughout the year, monthly seasonality to observe changes between months, and annual mean discharge to detect long-term trends. These analyses are critical for effective water resource management, flood forecasting, and understanding hydrological patterns. 
 
 To further explore the analysis of return periods, flow duration curves, and seasonal averages, we invite you to follow along with our interactive demonstration in the provided Google Colab notebook. This hands-on notebook will guide you through the process, using real data from the Tensift River in Morocco. You can access and run the notebook directly in your browser:  
 

docs/forecast.md

@@ -1,6 +1,6 @@
 # Forecast Data
 
-The GEOGLOWS model produces ensemble streamflow forecasts using data from the ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble system. Forecasts are produced daily and are available by 12 PM UTC. Similar to the retrospective data, the units are in cubic meters per second. 
+RFS produces ensemble streamflow forecasts using data from the ECMWF (European Centre for Medium-Range Weather Forecasts) ensemble system. Forecasts are produced daily and are available by 12 PM UTC. Similar to the retrospective data, the units are in cubic meters per second. 
 
 Each forecast includes a **50+1 member ensemble**:  
 - **1 baseline (control)** prediction  

docs/forecasted-bias-correction.md

@@ -1,6 +1,6 @@
 # Forecast Bias Correction
 
-The **GEOGLOWS model** applies bias correction to its forecast data by assuming the forecast shares the same biases as the retrospective simulation. This process involves mapping forecasted streamflow values to a non-exceedance probability using the historical simulation's flow duration curve and then replacing the forecasted values with corresponding values from the observed flow duration curve.
+RFS applies bias correction to its forecast data by assuming the forecast shares the same biases as the retrospective simulation. This process involves mapping forecasted streamflow values to a non-exceedance probability using the historical simulation's flow duration curve and then replacing the forecasted values with corresponding values from the observed flow duration curve.
 
 ![forecasts](forecast-bias-correction.png)
 

docs/retrospective.md

@@ -1,14 +1,14 @@
 # Retrospective Data
 
-The retrospective simulation from the River Forecasting System provides a deterministic dataset with daily resolution, offering over 85 years of historical streamflow data from January 1, 1940 to the near present. Each of the 6 million river segments in the GEOGLOWS model has its own retrospective time series available for download. The plot below shows an example of the retrospective data for one river.
+The retrospective simulation from RFS provides a deterministic dataset with daily resolution, offering over 85 years of historical streamflow data from January 1, 1940 to the near present. Each of the 6 million river segments in RFS has its own retrospective time series available for download. The plot below shows an example of the retrospective data for one river.
 
 ![image](retrospective_graph.png)
 
 ---
 
 The dataset is deterministic, which means that there is no ensemble, but rather just one best guess of the average flows over the time period. The start of the time period is given in UTC time. The value given represents the average flow starting at that time until the next timestep. All flow values are in cubic meters per second.  
 
-The inputs to the hydrology model lag from real time by 5 days. Once a week, on Sunday at midnight UTC, the dataset is updated. On that Sunday, GEOGLOWS data will cover up until 5 days ago. On Saturday night, the lag will have accumulated to 12 days.  
+The inputs to RFS lag from real time by 5 days. Once a week, on Sunday at midnight UTC, the dataset is updated. On that Sunday, RFS will cover up until 5 days ago. On Saturday night, the lag will have accumulated to 12 days.  
 
 The retrospective data is available in several different time increments. The data was calculated at an hourly temporal resolution. Then the hourly timesteps were averaged to provide a daily average, monthly average, and yearly average time series.   
 
@@ -19,7 +19,7 @@ The monthly average data is available in two different formats. One is optimized
 
 ## Runoff Data and Flow Estimates
 
-The runoff data is created by using the runoff data from the **ECMWF ERA5 reanalysis dataset** as an input to the River Forecasting System. Then, a modification of Muskingum-Cunge routing is applied using a method called RAPID to route the water through the stream network, providing the flow estimates.
+The runoff data is created by using the runoff data from the **ECMWF ERA5 reanalysis dataset** as an input to RFS. Then, a modification of Muskingum-Cunge routing is applied using a method called RAPID to route the water through the stream network, providing the flow estimates.
 
 The inputs to the model are derived from reanalysis meteorology data, including satellite and gauge-based measurements, which are assimilated to reconstruct the best possible historical precipitation, evaporation, and other hydrological variables. No river gauge data are assimilated during the routing step, ensuring a uniform model-driven approach.