---------------------------------------------------------------------
DATASET OVERVIEW

Dataset Title: CONUS Daily Streamflow Reanalysis, Version 2 (DayflowV2)

Investigators: Ganesh Ghimire, ghimiregr@ornl.gov, ORCID:0000-0002-4284-3941; Shih-Chieh Kao, kaos@ornl.gov ORCID:0000-0002-3207-5328; 
			   Sudershan Gangrade, gangrades@ornl.gov, ORCID:0000-0002-0730-0810

Point of Contact: Shih-Chieh Kao, kaos@ornl.gov, Oak Ridge National Laboratory

Summary: The DayflowV2 Dataset is a historical streamflow reanalysis dataset reconstructed for a 40-year period (1980-2019) using multiple forcings. 
         DayflowV2 provides both daily and monthly scale naturalized and assimilated streamflow data at about 2.7 million NHDPlusV2 stream reaches in the 
		 conterminous US (CONUS)along with a comprehensiove summary of associated performance metrics.  

Keywords: Streamflow Reanalysis, VIC-RAPID Modeling Framework, AORC, STAGE4, National Water Model, Extremes, High-performance computing 

Acknowledgments: The dataset was produced with funding from the US Department of Energy, Water Power Technology Office.

Related Publication: Ghimire et al. (2022) and a manuscript under review

Related Datasets: https://hydrosource.ornl.gov/dataset/dayflow-V1
---------------------------------------------------------------------
DATASET CHARACTERISTICS

Spatial Resolution: NHDPlusV2 stream reaches

Projection Information: N/A

Temporal Resolution: daily and monthly

Temporal Coverage: DaymetV4 (1980-2015), Stage-IV i.e., ST4 and its hybrids (2002-2019), and AORC and its hybrids (1980-2019)

File Format: .nc, .csv 

File Naming Convention: 
	For Daymet, each data file is in the format of VIC4_RAPID_Obs2015C_HUC8Type_1980_2015.nc
	For others, each data file is in the format of VIC4_RAPID_HUC8Type_Forcing2019_year.nc  
	where,				HUC8 - 8-digit hydrologic unit code
						Type - Simulation type (N for naturalized and C for assimilated)
						Forcing - meteorologic forcing used
						year - simulation year

File Descriptions: See FieldDescriptors.csv
---------------------------------------------------------------------
APPLICATION & DERIVATION 
These data supplement limited streamflow gauge observations at the Conterminous US scale and provide support to long-term water resources planning, 
ecological system conservation, flood resilience and mitigation, food-energy-water nexus studies, among others.

---------------------------------------------------------------------
QUALITY ASSESSMENT 

Estimate of Uncertainty: The Dayflow dataset uses multiple inputs, including multiple meteorological forcings, land and soil information, and river network information
that are not free of uncertainties which could propagate to the streamflow outputs. Observational uncertainties associated with the streamflow measurements 
also affect not only the assimilated streamflow outputs but also overall performance evaluation. At this point, RAPID algorithm used to produce DayflowV2 also
does not account for the lakes/reservoir releases. We address it to a degree with hierarchial streamflow assimilation framework. The dataset is fully quality 
controlled without any flags that the authors are aware of. 
  
---------------------------------------------------------------------
DATA ACQUISITION, MATERIALS & METHODS
The calibrated Variable Infiltration Capacity (VIC) model parameters used in this dataset generation is from Oubeidillah et al. (2014) and Naz et al. (2016).

---------------------------------------------------------------------
CHANGE LOG
This section will be updated with the release of newer versions.

---------------------------------------------------------------------
REFERENCES
- Ghimire, G.R., Hansen, C., Gangrade, S., Kao, S.C., Thornton, P., and Singh, D. 2022: Insights from Dayflow: A historical streamflow reanalysis dataset 
  for the Conterminous United States. Water Resources Research https://doi.org/10.1029/2022WR032312.
- Naz, B. S., Kao, S. C., Ashfaq, M., Rastogi, D., Mei, R., & Bowling, L. C. (2016). Regional hydrologic response to climate change in the conterminous 
  United States using high-resolution hydroclimate simulations. Global and Planetary Change, 143, 100–117. https://doi.org/10.1016/j.gloplacha.2016.06.003.
- Oubeidillah, A. A., Kao, S.-C., Ashfaq, M., Naz, B. S., & Tootle, G. (2014). A large-scale, high-resolution hydrological model parameter data set for 
  climate change impact assessment for the conterminous US. Hydrology and Earth System Sciences, 18(1), 67–84.   

---------------------------------------------------------------------
SUPPLEMENTAL FILES
- FieldDescriptors.csv
- README.txt

---------------------------------------------------------------------