Reach Scale Hydrology

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Global Reach-scale A priori Discharge Estimates for SWOT

GRADES

The Global Reach-scale A priori Discharge Estimates for SWOT is a model-derived daily discharge database for ~2.94 million vector river reaches derived from MERIT-Hydro (version 0.0) for 1980-2013. The modeling chain includes the VIC land surface model (0.25°, daily) and RAPID river routing model. The precipitation forcing input comes from the MSWEP (version 2.1) and the other meteorological fields from the NCEP CFSR reanalysis.

Production Method

The modeling system consists primarily of a land surface model VIC, a river routing model RAPID, a calibration procedure, and a bias-correction (post-processing) procedure:

Forcing Inputs

For the precipitation forcing, a recently published global 0.1° and 3‐hourly precipitation dataset MSWEP 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 CFSR.

Runoff Simulation - VIC Land Surface Model

VIC LSM is used for runoff simulation, where model parameter calibration and bias correction (BC) are performed against machine learning (ML)‐derived, global runoff characteristic maps Global Streamflow Characteristic Dataset (GSCD) (Beck et al., 2015; hereafter Qc maps). More specifically, GSCD regionalized runoff signatures (i.e., runoff values at specific levels) to the global scale by regressing discharge observations from 3,000–4,000 naturalized catchments to 20 climatic and physiographic predictors through neural network training, which generated 17 global Qc maps.

Model Calibration

Instead of directly calibrating VIC against limited gauge observation as in a traditional approach, we calibrate VIC at each 0.25° grid cell (a total of ~0.24 million grid cells for the globe) independently, using the baseflow index, climatology runoff (QMEAN), and runoff percentiles (Q10 and Q90) as the reference. Three sensitive VIC parameters controlling the generation of surface and subsurface flow are selected for calibration, including the variable infiltration curve parameter (bi), thickness of soil layer 2 (thick2), and fraction of the maximum velocity of base flow at which nonlinear base flow begins (Ds). The Shuffled Complex Evolution (SCE‐UA) algorithm is employed to find the optimal parameter set for each grid cell. More details of this calibration method can be found in Yang et al. (2019).

Bias Correction (Post-processing)

Here we develop a new postprocessing approach to further reduce the model biases that are still present after calibration, and details of the new BC method named “sparse cumulative density function (CDF) matchingcan be found by clicking the link.

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 are used for RAPID routing (~2.94 million, covering 60°S to 90°N) .

Validation

GRADES has been validated against 14,000+ daily gauges globally. The following figure shows the Kling-Gupta Efficiency (KGE) skill metrics and its three component metrics - Pearson correlation (CC), relative bias (RB, or PBIAS), and variability ratio (VR, or RV):

Lake/Dam Impacts

GRADES does not consider lakes or dams in its modeling chain. Their impacts are assessed and this figure shows that such impacts are minimal in terms of Pearson correlation, very mild on bias and mild on variability.

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Upgrade

We are updating the GRADES data archive to:

(1) re-base the discharge simulation on most recent release of MERIT-Hydro and the vector hydrography so derived (with bugs fixed);

(2) refine spatial and temporal resolution of VIC model implementation;

(3) replace CFSR meteorological fields with better/finer ERA5 products;

(4) extend the available period to end of the 2019.

The updated discharge data will be part of the Global Reach-level Flood Reanalysis (GRFR) dataset.

Reference

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

Lin, P., M. Pan, H. E., Beck, Y. Yang, D. Yamazaki, R. Frasson, C. H. David, M. Durand, T. M. Pavelsky, G. H. Allen, C. J. Gleason, and E. F. Wood, 2019: Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resources Research, https://doi.org/10.1029/2019WR025287.

Yang, Y., M. Pan, H. E. Beck, C. K. Fisher, R. E. Beighley, S.-K. Kao, Y. Hong, and E. F. Wood, 2019: In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics. Water Resources Research, 55, https://doi.org/10.1029/2018WR024178.

Related Presentations

LinPan_2019SWOT_mpan.pptx