Application of CryoSat-2 altimetry data for river analysis and modelling

Abstract

Abstract. Availability of in situ river monitoring data, especially of data shared across boundaries, is decreasing, despite growing challenges for water resource management across the entire globe. This is especially valid for the case study of this work, the Brahmaputra Basin in South Asia. Commonly, satellite altimeters are used in various ways to provide information about such river basins. Most missions provide virtual station time series of water levels at locations where their repeat orbits cross rivers. CryoSat-2 is equipped with a new type of altimeter, providing estimates of the actual ground location seen in the reflected signal. It also uses a drifting orbit, challenging conventional ways of processing altimetry data to river water levels and their incorporation in hydrologic-hydrodynamic models. However, CryoSat-2 altimetry data provides an unprecedentedly high spatial resolution. This paper suggests a procedure to (i) filter CryoSat-2 observations over rivers to extract water-level profiles along the river, and (ii) use this information in combination with a hydrologic-hydrodynamic model to fit the simulated water levels with an accuracy that cannot be reached using information from globally available digital elevation models (DEMs) such as from the Shuttle Radar Topography Mission (SRTM) only. The filtering was done based on dynamic river masks extracted from Landsat imagery, providing spatial and temporal resolutions high enough to map the braided river channels and their dynamic morphology. This allowed extraction of river water levels over previously unmonitored narrow stretches of the river. In the Assam Valley section of the Brahmaputra River, CryoSat-2 data and Envisat virtual station data were combined to calibrate cross sections in a 1-D hydrodynamic model of the river. The hydrologichydrodynamic model setup and calibration are almost exclusively based on openly available remote sensing data and other global data sources, ensuring transferability of the developed methods. They provide an opportunity to achieve forecasts of both discharge and water levels in a poorly gauged river system.

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

“…Performance slightly decreases in the validation period, in particular for the Ngounié virtual stations and the FEWS-RFE model. When comparing the simulated water depth amplitudes to those observed at each station, the RMSD is 0.74 m for the TRMM model and 0.87 m for FEWS-RFE, corresponding to 0.85 and 0.94 times the standard deviation of annual water height amplitude (Table 7).This is comparable to the study by Schneider et al (2017), in which they obtained an average RMSE of 0.83 m for the Brahmaputra after calibrating the river cross sections in a hydrodynamic model against Envisat virtual stations. Figure 10 shows the water height fluctuations at two of the virtual stations.…”

“…Due to its long repeat period, CryoSat-2 samples more often during certain seasons over different parts of the river. Schneider et al (2017) obtained an RMSD of 2.5 m between simulated water heights and CryoSat-2 observations over the Brahmaputra -thus, we deem the obtained results satisfactory in light of the available information.…”

“…Therefore, recent studies have focused on densifying the altimetry dataset by incorporating observations from drifting ground track missions as well (Schneider et al, 2017). The long repeat period results in a higher spatial resolution, as more points are sampled along the river.…”

“…However, in many river basins, in situ data are limited or insufficient (Hannah et al, 2011). Instead, remote sensing observations of hydrological state variables such as river level (Schneider et al, 2017), total water storage or soil moisture (Milzow et al, 2011;Xie et al, 2012;Abelen and Seitz, 2013) can be used to calibrate and validate the hydrological model performance and improve parameter estimation. Alvarez-Garreton et al (2014) improved model parametrization by exploring multiple hydrological state variables in a multi-objective calibration.…”

“…Water masks with sufficiently high resolution are required to extract the observations properly. Schneider et al (2017) used a water mask based on the Landsat normalized difference vegetation index (NDVI) observations to extract CryoSat-2 observations over the Brahmaputra. However, optical data are not suitable in tropical regions with frequent cloud cover.…”