# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 2 Contributed Papers
### Flood Simulations and Predictions  
In this category, four papers are published. [PERSON] et al. [10] conducted a flash flood warnings study in Yunnan Province, China using NASA's Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG) precipitation products. They tested two GPM-IMERG products: The near-real-time IMERG Early run product (IMERG-E) and the post-real-time IMERG Final run product (IMERG-F) with a 6-h temporal resolution. The results show that MERG-F is better than IMERG-E over the study area, indicated by an hourly R of 0.46 and relative bias of 23%. Furthermore, the IMERG-F results are well corresponding to the gauge data when using the Rainfall Triggering Index (RTI) model for calibration, suggesting that MERG-F is suitable for flash flood warnings. [PERSON] et al. [11] investigated if the prediction of flash flood can be improved for mountainous catchments of northern China using the WRF-3 DVAR module through coupled atmospheric-hydrologic systems. Compared to the baseline (openloop) model run, the assimilation improved the accuracy of rainfall accumulation as well as provided more accurate flood forecasting. Based on the grid-based Hebei model, an atmospheric-hydrological coupling system was established and performed by predicting the flash flood as well as obtaining the best performance of Nash-Sutcliffe Efficiency (NSE) = 0.874 after assimilation, compared to NSE = 0.64 before assimilation. Moreover, the authors pointed out the need to carefully transfer hydrological parameters, since the locally derived hydrodynamic parameters may not be applicable to mesoscale areas. [PERSON] et al. [12] proposed a modelling framework for the urban flood analysis in ungauged catchments using short-term and high-resolution rainfall data. The framework includes three steps. First, generate extreme rainfall events using a rainfall generator named RainyDay together with a 9-year record of hourly, 0.1\({}^{\circ}\) remotely sensed rainfall data. Second, simulate runoff using an Urban Hydrological Model (SWMM) under different rainfall return periods and durations. Third, analyze urban flood using flood indicators, such as flood time, maximum rainfall rates, and total maximum rainfall volume. This framework was tested in Guangzhou city, China. The results show that a combination of RainyDay and short-term remotely sensed rainfall data can expand the rainfall records for urban hydrological simulations and predictions. Furthermore, the proposed framework shows a good performance for runoff process simulation, especially for high return periods or long durations (NSE > 0.90), demonstrating that the proposed framework has potential for urban flood analysis in ungauged catchments. [PERSON] et al. [13] analyzed and simulated runoff processes in a heavily regulated river basin, the Lhasa River where there are Zhikong and Pangduo hydropower dams changing hydrological regimes. The analysis indicates that the Lhasa River streamflow shows stronger variations during 2000-2016, compared to pre-2000. The Zhikong hydropower plant and the Pangduo power plant began operations in 2006 and 2013, respectively, which strongly influence the Lhasa River streamflow. The modelling results indicate that the SWAT hydrological model is capable of simulating the streamflow under reservoir influence, and can be used for predicting future streamflow. The predicted streamflow has a similar behavior to the observation while decreasing in the years from 2017 to 2025, indicating that the hydrological regimes in this region are simultaneously affected by climate change and anthropogenic impacts. [PERSON] et al. [14] proposed a new method using an Unmanned Aerial Vehicle (UAV) combined with the incipient motion of stone to calculate the peak streamflow of ephemeral rivers in northwestern China. Critical initial velocities of moving stones were estimated using two methods: Logarithmic and exponential velocity distribution methods.