Inversion of river discharge from remotely sensed river widths: a critical assessent at three-thousand global river gauges

Accurately estimating river discharge from satellite-derived river hydraulic variables (e.g., width, height, and slope) is the overarching goal of the remote sensing of discharge (RSQ) community. Numerous past studies have developed and intercompared different RSQ algorithms to determine favorable RSQ conditions, yet relatively few have focused on tailoring RSQ to a wide range of river forms globally. As the RSQ community is now ready to expand to the global scale given advances in computing power, sensors, and the upcoming launch of the Surface Water and Ocean Topography (SWOT) satellite mission, a much broader geographic view of RSQ accuracy should be prioritized towards “better generalizability” instead of “higher accuracy at limited places”. To help close this gap, we extracted multi-temporal river widths from 357,389 Landsat scenes at >3,000 river reaches globally, and used them to estimate discharge using the Bayesian AMHG-Manning (BAM) algorithm and the geomorphologically-enhanced variant (geoBAM). Our daily discharge inversions (1984–2019) using the ‘off the shelf’ algorithmic parameters exhibited acceptable performance (positive Kling-Gupta Efficiency, KGE) at 27% of the gauges for BAM and 39% of the gauges for geoBAM, amounting to ~1,000 successful inversions. Exploratory analyses to discover controls on accuracy revealed that the inversions are most sensitive to a channel shape parameter b, among six factors assessed. By introducing richer prior knowledge on the discharge seasonality and intra-seasonal variability, 1400–2000 successful inversions were derived, and further constraining the factors to their optimal ranges led to a median KGE of 0.33 for >600 gauges, up from -0.10 for the entire set, which highlights the promising potential for global RSQ. On top of the encouraging results, we present two contrasting cases that demonstrate how the relative effectiveness between RS observations and prior discharge work together to influence the inversion, pointing toward the need to better characterize the effectiveness between RS and priors for spatio-temporally explicit RSQ improvements. Overall, our critical assessment of the BAM/geoBAM algorithms shows promise for global RSQ and reveals a lower bound of SWOT discharge accuracy. It also highlights the value of assessing RSQ at global scales as we move into a new era upon SWOT’s launch.

搜集汇总

背景与挑战

背景概述

该数据集是一个全球尺度的遥感河流流量反演研究，基于1984-2019年的Landsat数据，在3000多个河流测站上应用BAM和geoBAM算法估算流量。其关键特点包括评估算法性能（如27%-39%的测站表现可接受），并揭示渠道形状参数是影响精度的主要因素，旨在为全球遥感流量估算和SWOT卫星任务提供基础参考。