The present study aims to evaluate three global satellite precipitation products [TMPA 3B42, version 7 (3B42 V7); TMPA 3B42 real time (3B42 RT); and Climate Prediction Center morphing technique (CMORPH)] during 2003–12 for multiscale hydrologic applications—including annual water budgeting, monthly and daily streamflow simulation, and extreme flood modeling—via a distributed hydrological model in the Yangtze River basin. The comparison shows that the 3B42 V7 data generally have a better performance in annual water budgeting and monthly streamflow simulation, but this superiority is not guaranteed for daily simulation, especially for flood monitoring. It is also found that, for annual water budgeting, the positive (negative) bias of the 3B42 RT (CMORPH) estimate is mainly propagated into the simulated runoff, and simulated evapotranspiration tends to be more sensitive to negative bias. Regarding streamflow simulation, both near-real-time products show a region-dependent bias: 3B42 RT tends to overestimate streamflow in the upper Yangtze River, and, in contrast, CMORPH shows serious underestimation in those downstream subbasins while it is able to effectively monitor streamflow into the Three Gorges Reservoir. Using 394 selected flood events, the results indicate that 3B42 RT and CMORPH have competitive performances for near-real-time flood monitoring in the upper Yangtze, but for those downstream subbasins, 3B42 RT seems to perform better than CMORPH. Furthermore, the inability of all satellite products to capture some key features of the July 2012 extreme floods reveals the deficiencies associated with them, which will limit their hydrologic utility in local flood monitoring.

A popular way to forecast streamflow is to use bias-corrected meteorological forecasts to drive a calibrated hydrological model, but these hydrometeorological approaches suffer from deficiencies over small catchments due to uncertainty in meteorological forecasts and errors from hydrological models, especially over catchments that are regulated by dams and reservoirs. For a cascade reservoir catchment, the discharge from the upstream reservoir contributes to an important part of the streamflow over the downstream areas, which makes it tremendously hard to explore the added value of meteorological forecasts. Here, we integrate meteorological forecasts, land surface hydrological model simulations and machine learning to forecast hourly streamflow over the Yantan catchment, where the streamflow is influenced by both the upstream reservoir water release and the rainfall–runoff processes within the catchment. Evaluation of the hourly streamflow hindcasts during the rainy seasons of 2013–2017 shows that the hydrometeorological ensemble forecast approach reduces probabilistic and deterministic forecast errors by 6 % compared with the traditional ensemble streamflow prediction (ESP) approach during the first 7 d. The deterministic forecast error can be further reduced by 6 % in the first 72 h when combining the hydrometeorological forecasts with the long short-term memory (LSTM) deep learning method. However, the forecast skill for LSTM using only historical observations drops sharply after the first 24 h. This study implies the potential of improving flood forecasts over a cascade reservoir catchment by integrating meteorological forecasts, hydrological modeling and machine learning.

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998–2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002–2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability.

Western‐central Europe experienced the most severe June–July heat on record in 2019, with several heatwaves occurring over the most densely populated regions. Highest 3‐day averaged daily mean temperature in June–July averaged over the region exceeds normal by 4.7°C, which is estimated to be a 1‐in‐283‐year event over the 1950–2014 climate. The driver and future likelihood of this extreme heat in a changing climate have drawn extensive attention. Based on the newly released climate model data from the sixth Coupled Model Intercomparison Project (CMIP6), we find that anthropogenic climate change has caused a sevenfold increase in the likelihood of the extreme heat over 1950–2014 climate, and even a 23‐fold increase since 1980s. Such extreme heat will become more frequent in the future, with return periods of 1.8–7.2 years under future emission and societal development scenarios. Without sufficient adaptation strategies, such extreme heat would become more widespread, long‐lasting, and severe over Europe.