No abstract

Spatial and temporal variabilities of long-term (1961-2013) diurnal temperature range (DTR) are examined in the Tibetan Plateau (TP) based on the 71 observational stations. The relative regional contributions to DTR in the TP are studied among maximum temperature, minimum temperature, total cloud cover (TCC), and atmospheric teleconnections. The regional annual mean DTR (average of the 71 stations) is 14.17 ∘ C, with a clear maximum in winter (16.35 ∘ C) and minimum in summer (12.62 ∘ C). During 1961-2013, the DTR in the TP declines before the 1980s and shows mute change afterwards, with an annual rate of −0.20 ∘ C decade −1 calculated by the Mann-Kendall method. The trend in DTR is primarily a consequence of greater warming in minimum temperature than maximum temperature. In summer, there are significant negative correlations between the TCC and DTR in the TP, suggesting that the decreases in the DTR are associated with variations of TCC in the region. The atmospheric circulation composite analysis between strongly positive and negative DTR in summer in the TP reveals that during the low DTR period the TP has more water vapour flux, stronger temperature advection, and strengthened southerly wind. This suggests that the atmospheric circulations have contributed to the trends in the DTR, but it is difficult to account for the specific contributions. Further investigations of the impact of global warming on the DTR in the TP are still required.

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.

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.

No abstract