ABSTRACT:

The Rapid Forcings Retrieval (RFR) tool gets the hourly Forcings in CSV and GeoTIFF formats for small and large-scale areas to drive hydrological models. The CSV is the main output for driving the dynamic hydrologic process, while the GeoTIFF is an average of the selected period. The RFR retrieves Forcings for specific United States Geological Survey (USGS) Hydrologic Units watersheds, CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) basins, and points (gauged and ungauged locations). RFR relies on the North American Land Data Assimilation System (NLDAS) hosted on Google Earth Engine. NLDAS Forcings (Precipitation, Evapotranspiration, Temperature, Specific Humidity, Surface Pressure, Convective Fraction, wind, Potential Energy, Long and Shortwave Radiation) are vital model inputs for discharge prediction. Users can quickly select a specific date range and area of interest to subset Forcings which are then served into a model for streamflow predictions. RFR allows users to export specific Forcings at a time, keeping the interface clean while preventing crowding of panels. The RFR intends to support the current Forcings source for the NOAA Office of Water Prediction (OWP) and does not act as a replacement for the current source. RFR will be useful to Hydrologists, Environmental Resource Managers, and other scientists using Forcings. Currently, RFR can help support the ongoing Next Generation (NEXTGEN) Framework hydrological models testing and validations. A use case begins with (1) visualizing watershed scales, (2) digitizing specific watershed boundaries, (3) selecting a specific date range, (4) choosing forcing variables to output, and (5) exporting forcings as desired by the user. Users must note that all output datasets begin from 00:00 UTC at the start date and ends at 23:00 UTC at the entered end date.

To use RFR visit:
Ekpetere, K. O., X. Li, J. Frame (2022). The Rapid Forcing Retrieval (RFR), HydroShare, http://www.hydroshare.org/resource/adc37a792a6144c9a1d45e05621e4230

ABSTRACT:

The Rapid Forcings Retrieval (RFR) tool gets the hourly Forcings in CSV and GeoTIFF formats for small and large-scale areas to drive hydrological models. The CSV is the main output for driving the dynamic hydrologic process, while the GeoTIFF is an average of the selected period. The RFR retrieves Forcings for specific United States Geological Survey (USGS) Hydrologic Units watersheds, CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) basins, and points (gauged and ungauged locations). RFR relies on the North American Land Data Assimilation System (NLDAS) hosted on Google Earth Engine. NLDAS Forcings (Precipitation, Evapotranspiration, Temperature, Specific Humidity, Surface Pressure, Convective Fraction, wind, Potential Energy, Long and Shortwave Radiation) are vital model inputs for discharge prediction. Users can quickly select a specific date range and area of interest to subset Forcings which are then served into a model for streamflow predictions. RFR allows users to export specific Forcings at a time, keeping the interface clean while preventing crowding of panel.

To use RFR Web App, visit:
Ekpetere, K. O., X. Li, J. Frame (2022). The Rapid Forcing Retrieval (RFR), HydroShare, http://www.hydroshare.org/resource/adc37a792a6144c9a1d45e05621e4230

ABSTRACT:

Built on Google Earth Engine development, the PMP calculator computes the probable maximum precipitation (PMP) using the WMO Hershfield approach as well as the underlying IMERG, CHIRPS, & TRMM aggregate precipitation and Depth Duration Frequency Data for the period (10/2000-10/2020). Thanks to the Google Earth Engine team for the fantastic platform.

ABSTRACT:

The Probable Maximum Precipitation (PMP) Datasets in Geotiff format at the 0.5-hr, 1-hr, 2-hr, 3-hr, 6-hr, 12-hr, 24-hr, 2-day, and 3-day are statistically derived based on World Meteorological Organization (WMO)’s endorsed Hershfield PMP estimation technique using IMERG’s 30-min precipitation dataset. The Google Earth Engine’s script for assessing and interacting with the datasets is also provided.