| 31 Jul 2026
Globally Applicable Discharge Reanalysis using SWOT observations
Abstract. Global river discharge observations are critical for climate and hydrological research, used for water resource management, risk mitigation, infrastructure and environment conservation, among many other areas. However, existing observations are limited in availability, accuracy and spatial extent, with uneven geographical distribution and high levels of uncertainty often recorded. The NASA SWOT mission provides the opportunity to fill this gap, providing water level observations for all rivers exceeding 100 m in width worldwide. Combined with the daily discharge model GRADES-hydroDL, we present a statistical method to produce a set of global virtual gauging stations with the same temporal resolution as the satellite observations. The high level of accuracy that the SWOT water surface elevation observations allows us to improve the dynamics of discharge estimation over the input discharge model. For our validation gauges, where we compared modelled discharge to observed values, 66 % of Pearson R values are larger than 0.9, showing the method’s skill at replicating temporal patterns in recorded flow. The median Root Mean Square Error, RMSE, of the input discharge set is 36.3 m3 s−1 larger than that of our proposed method, and the NSE 0.3 lower. However, our method fails to improve the bias; the median absolute normalised bias is 0.10 (10 %) higher in our model than the input model, indicating that while the inclusion of Earth Observation can greatly improve the discharge time series dynamics, the trade-off is an increased bias in the distribution. Nevertheless, as the record of SWOT observations increases in length, these results should improve and the margins of bias difference decrease. The result is a globally applicable reanalysis dataset of discharge, which will complement existing physically based models and ground observations, and can be widely used within global hydrological models.
This paper presents a simple, transparent statistical baseline for global discharge estimation from SWOT observations, complementing the more complex DAWG algorithms and greatly expanding spatial coverage. The data preprocessing is careful and the sensitivity analysis is systematic. The evaluation is also honest: a broad set of metrics, performance classification, R_G-stratified analysis, and clear acknowledgment of the bias trade-off and the uneven distribution of validation gauges. However, I have a few comments on this paper.
1. Is “reanalysis” the right term? In hydrology, reanalysis usually implies temporally and spatially continuous products with data assimilation. However, SWOT-DR estimates discharge only at SWOT overpass times and only for non-extreme conditions, without assimilation. I therefore question whether “reanalysis” is an appropriate label for this product.
2. The monotonicity assumption. The method structurally assumes a strictly monotonic WSE–discharge relationship (Sect. 3.4), but this assumption is only tested indirectly— via monotonicity between gauge and SWOT water levels (Sect. 2.4.1) at 399 gauged reaches. For the global 92,955 reaches the assumption remains unverified. Backwater, tidal, ice, dam regulation, and hysteresis can all violate it; river width is available but unused. I recommend stating this assumption explicitly, discussing its failure modes, and tempering the “globally applicable” claim.
3. High R is partly inherited, not an independent contribution of the method. Because SWOT-DR discharge is a monotonic transform of observed WSE, its high correlation with gauged discharge largely reflects the information content of SWOT WSE itself, rather than independent model skill. I suggest explicitly separating observation information content from method gain, e.g., by benchmarking against a null model (direct WSE–discharge regression or a rank-matching version of GRADES) and by softening abstract wording such as "showing the method's skill."