Evaluation of Historic and Operational Satellite Radar AltimetryMissions for Constructing Consistent Long-term Lake Water LevelRecords

Abstract. Thirteen satellite missions have been launched since 1985, with different types of radar altimeters onboard. This study intends to make a comprehensive evaluation of historic and currently operational satellite radar altimetry missions for lake water level retrieval over the same set of lakes and to develop a strategy for constructing consistent long-term water level records for inland lakes at global scale. The lake water level estimates produced by different retracking algorithms (retrackers) of the satellite missions were compared with the gauge measurements over twelve lakes in four countries. The performance of each retracker was assessed in terms of the data missing rate, the correlation coefficient r, the bias, and the Root Mean Square Error (RMSE) between the altimetry-derived lake water level estimates and the concurrent gauge measurements. The results show that the model-free retrackers (e.g. OCOG/Ice-1/Ice) outperform the model-based retrackers for all missions, particularly over small lakes. Among the satellite altimetry missions, Sentinel-3 gave the best results, followed by SARAL. ENVISat has slightly better lake water level estimates than Jason-1 and -2, but its data missing rate is higher. For small lakes, ERS-1 and ERS-2 missions provided more accurate lake water level estimates than Topex/Poseidon mission. In contrast, for large lakes Topex/Poseidon is a better option due to its lower data missing rate and shorter repeat cycle. GeoSat and GeoSat Follow-On (GFO) both have extremely high data missing rate. Although several contemporary radar altimetry missions provide more accurate lake level estimates than GFO, GeoSat was the sole radar altimetry mission between 1985 and 1990 that provided the lake water level estimates. With a full consideration of the performance and the operational duration, the best strategy for constructing long-term lake water level records should be a two-step bias correction and normalization procedure. In the first step, use Jason-2 as the initial reference to estimate the systematic biases with Topex/Poseidon, Jason-1 and Jason-3 and then normalize them to form a consistent Topex/Poseidon-Jason series. Then, use Topex/Poseidon-Jason series as the reference to estimate and remove systematic biases with other radar altimetry missions to construct consistent long-term lake water level series for ungauged lakes.