Hydro-meteorological datasets are key components for understanding physical hydrological processes, but the scarcity of observational data hinders their potential application in poorly gauged regions. Satellite-retrieved and atmospheric reanalysis products exhibit considerable advantages in filling the spatial gaps in in-situ gauging networks and are thus forced to drive the physically lumped hydrological models for long-term streamflow simulation in data-sparse regions. As machine learning (ML)-based techniques can capture the relationship between different elements, they may have potential in further exploring meteorological predictors and hydrological responses. To examine the application prospects of a physically constrained ML algorithm using earth observation data, we used a short-series hydrological observation of the Hanjiang River basin in China as a case study. In this study, the prevalent modèle du Génie Rural à 9 paramètres Journalier (GR4J-9) hydrological model was used to initially simulate streamflow, and then, the simulated series and remote sensing data were used to train the long short-term memory (LSTM) method. The results demonstrated that the advanced GR4J9–LSTM model chain effectively improves the performance of the streamflow simulation by using more remote sensing data related to the hydrological response variables. Additionally, we derived a reservoir operation model by feeding the LSTM-based simulation outputs, which further revealed the potential application of our proposed technique.

Under the background of global climate change, drought is causing devastating impacts on the balance of the regional water resources system. Hydrological drought assessment is critical for drought prevention and water resources management. However, in China to assess hydrological drought at national scale is still challenging basically because of the difficulty of obtaining runoff data. In this study, we used the state-of-the-art passive microwave remote sensing techniques in river runoff modelling and thus assessed hydrological drought in Mainland China in 1996–2016. Specifically, 79 typical hydrological stations in 9 major basins were selected to simulate river runoff using the M/C signal method based on a high-resolution passive microwave bright temperature dataset. The standardized runoff index (SRI) was calculated for the spatial and temporal patterns of hydrological drought. Results show that passive microwave remote sensing can provide an effective way for runoff modelling as 92.4% and 59.5% of the selected 79 stations had the Pearson correlation coefficient (R) and the Nash-Sutcliffe efficiency coefficient (NS) scores greater than 0.5. Especially in areas located on Qinghai-Tibet Plateau in the Inland and the Southwest River Basin, the performance of the M/C signal method is quite outstanding. Further analysis indicates that stations with small rivers in the plateau areas with sparse vegetation tend to have better simulated results, which are usually located in drought-prone regions. Hydrological drought assessment shows that 30 out of the 79 stations present significant increasing trends in SRI-3 and 18 indicate significant decreasing trends. The duration and severity of droughts in the non-permanent dry areas of the Hai River Basin, the middle reaches of the Yangtze River Basin and the Southwest of China were found out to be more frequent and severe than other regions. This work can provide guidance for extending the applications of remote sensing data in drought assessment and other hydrological research.

Since the impoundment of the Three Gorges Project, the downstream hydrology and river dynamics have been modified. The Yichang–Chenglingji Reach (YCR), as a part of the mainstream of the Middle Yangtze River, has consequently been significantly scoured, which has resulted in stream trenching and section enlargements, without showing any obvious trend in flood level variation, however. This phenomenon can be caused by the increase in riverbed resistance due to river geomorphological change and bottomland vegetation development and the backwater effect of Dongting Lake. To investigate how these factors influence the flood water levels, this study analyzed the variations in the influencing factors based on observational data, theoretical analysis and mathematical modelling, including river channel scouring, riverbed resistance, and the influence of Dongting Lake backwater. Then, the impact of these factors on flood levels was evaluated, followed by a comparative analysis of the effects of various factors. The results show that both the flood backwater height (ΔZ) and the backwater influence range (L) are positively correlated with the outflow intensity (T) at the Chenglingji station. The backwater effect decreases gradually with increasing upstream distance, and the influence on the upstream reach can extend up to Shashi city. It was also indicated that the increase in riverbed resistance due to bottomland vegetation development and river geomorphology are dominant factors in inhibiting flood level declines in the YCR, while the backwater of Dongting Lake just affects local regions. This study can provide a better understanding of the flood level changes of the YCR and thus contribute to flood control and riverbank protection of the Yangtze River in the future.