Reach Scale Hydrology

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Global Reach-scale A priori Discharge Estimates for SWOT

GRADES-hydroDL

The GRADES-hydroDL (Yang et al., 2023) is a major upgrade to the original GRADES (Global Reach-scale A priori Discharge Estimates for SWOT) model-derived daily discharge database for millions of vector river reaches from 1980-present. The major upgrades to GRADES include:

The VIC land surface model (0.25°, daily) that is used to derive gridded runoff fields is replaced with the Long Short-Term Memory (LSTM) model developed by the hydroDL project following Feng et al., 2020.
A different version of MERIT-Basins (MERIT_Hydro_v07_Basins_v01) hydrography is used for routing, and the RAPID river routing model with updated parameters is used.
The precipitation forcing input is taken from a newer version of MSWEP (version 2.8) and the other meteorological fields from the ERA5.
A near real-time (NRT) data stream (upon request) is available following the release of ERA5 every month, i.e., 1-2 months behind real time.

Summary

To expand the spatial coverage of the conventional Basin-scale Long Short-Term Memory (LSTM) model for river discharge estimation beyond pre-selected individual locations, we developed a discharge modeling scheme, Grid-scale LSTM-RAPID, to estimate discharge for every river reach worldwide. Grid-scale LSTM-RAPID extends the application of LSTM runoff estimation to the grid scale (0.25°), and then routes the grid-scale runoff over all reaches on a global river network using the RAPID routing model. It largely maintains the strong performance of Basin-scale LSTM over gauged basins and achieves a median Kling-Gupta Efficiency (KGE) of 0.653 for small basins out-of-sample both temporally and spatially with relatively better data quality, and a median KGE of 0.592 for other basins with larger areas and less data quality. Compared to Basin-scale LSTM, Grid-scale LSTM-RAPID loses about 0.03 in median KGE for basins out-of-sample in both time and space in exchange for global all-reach coverage without heavy cost. Despite this tradeoff, it significantly outperforms a well-calibrated process-based benchmark model. Using the new scheme, we created an improved global reach-level daily discharge dataset from 1980 to 2023 named GRADES-hydroDL.

See Yang et al., 2024 for more details.

Inputs

Dynamic inputs:

For the precipitation forcing, a recently published global 0.1° and 3‐hourly precipitation dataset MSWEP version 2.8 that optimally merges a range of gauge‐, reanalysis‐, and satellite‐based precipitation (Beck et al., 2019) is used. Other forcing variables (including min/max 2‐m air temperatures and 10‐m wind speed) are obtained from the ERA5. The monthly leaf area index (LAI) is from PROBAV VITO.

Static inputs:

10 sensitive attributes including climate, topography, and soil attributes.

Runoff Simulation - LSTM Model

River Routing

For the river network routing, the Routing Application for Parallel computatIon of Discharge (RAPID; David et al., 2011; David, 2019) is used due to its flexibility in dealing with vector river networks in a range of regional‐ to continental‐scale applications. Global vector river flowlines in MERIT-Basins version 1.0 (MERIT_Hydro_v07_Basins_v01) [caution: NOT MERIT_Hydro_v07_Basins_v01_bugfix1] are used for RAPID routing (~2.94 million, covering 60°S to 90°N).

Validation

Download (Google Drive Linux instructions)

Discharge: preliminary data available from this [Globus collection] (you will need a Globus account if you don't have one already with your institution and the signup is free). If the Globus collection is not reachable, you can also try this [Google Drive folder] which may be less friendly for slower connections due to large file size.

Note: It's essential to note that GRADES_hydroDL is still unpublished, and the accompanying paper is currently under review. Therefore, we kindly request that you exercise caution and refrain from redistributing the data without our explicit permission. If you want to use this pre-publication data for your research, please inform us (Yuan Yang yuy068@ucsd.edu and Ming Pan m3pan@ucsd.edu) first about how you intend to utilize the data and acknowledge our work appropriately in any outputs resulting from its use.

Underlying hydrography (version 1.0 of MERIT-Basins): [Google Drive] | [TPDC (users in China)]
Please refer to the Readme file to learn how to extract the dataset.

Reference

Please refer to the following paper(s) for the details of the description of this global discharge database:

Yang, Y., D. Feng, H. E. Beck, W. Hu, A. Sengupta, L. Delle Monache, R. H. Hartman, C. Shen, and M. Pan, 2023: Global Daily Discharge Estimation Based on Grid-Scale Long Short-Term Memory (LSTM) Model and River Routing. Water Resources Research, in review, preprint on ESS Open Archive.