Publications

Pham, HT; Marshall, L; Johnson, F (2021). Daily time series of river water levels derived from a seasonal linear model using multisource satellite products under uncertainty. JOURNAL OF HYDROLOGY, 602, 126783.

Abstract
Satellite altimetry helps to monitor continental water levels with global coverage and near-real time accessibility. However, a main drawback of satellite altimetry is its low acquisition frequency that hinders the use of altimeters in flood forecasting or reservoir operation processes. This study provides a new method to estimate daily river heights from multisource hydro-climate satellite products without using ground-based data. First, the approach develops a simple statistical model, a seasonal linear model, using satellite altimetry, land surface temperature (LST), satellite precipitation and satellite soil moisture products. Second, a Monte Carlo simulation is used to understand the model accuracy by attributing the model output errors to uncertainties from the multiple satellite products. For the first aim, the study shows that using high-frequency satellite data to improve the temporal resolution of the satellite radar altimetry is better than an approach merging multiple satellite altimetry data. With respect to the second objective, it is found that higher variances are found in seasonal transition periods where there are significant changes in satellite observations. The overall error of the model is attributed to both uncertainties of satellite input data and the interactions among their uncertainties. The results demonstrate the capability of using multisource satellite data to predict daily time series of river heights in data-poor areas. The results also highlight the importance of quantifying uncertainty to increase the reliability of flood forecasting models using satellite products.

DOI:
10.1016/j.jhydrol.2021.126783

ISSN:
0022-1694