Discharge Estimation With Improved Methods Using MODIS Data in Greenland: An Application in the Watson River

Abstract

Greenland's river discharge has important implications for the Greenland Ice Sheet (GrIS) mass balance, global sea-level rise, and climate change. However, long-term and continuous in-situ discharge data for Greenland are scarce. The water extent is an important proxy to estimate discharge using remote sensing, but previous studies on estimating the discharge in Greenland required in-situ reflectance data to construct the water extent and suffered from inefficient processing. Here, we derived the water extent solely from the Moderate Resolution Imaging Spectroradiometer (MODIS) daily reflectance product on the Google Earth Engine (GEE) cloud platform. To improve the accuracy and efficiency, we optimized the strategies for water extent estimation and the optimal gauge pixel selection. Our improved method was applied in the Watson River. The runoff data from the regional climate model RACMO2 were employed to compare with the estimated results. Our results provide the daily discharge of the Watson River from 2002 to 2021, covering the period when field observations are unavailable. The correlation coefficient (R) and the fractional root mean square error (fRMSE) between the daily estimated discharge and the in-situ discharge are 0.69 and 0.73, respectively, whereas the R and fRMSE are 0.85 and 0.53 at a monthly timescale, respectively. Comparisons between our results and the RACMO2 runoff data suggest that the RACMO2 may generally underestimate the annual ice sheet melt runoff but overestimates the monthly runoff in July by 30% on average. The proposed method is highly automated, efficient and has the potential to be applied in other rivers with field measurements to provide continuous and long-term discharge observations. It contributes to a better understanding of the response of the GrIS to climate change