Lack of streamflow data limits applications of hydrological models in Qinghai-Tibet Plateau. This study explored the value of using remote sensing evapotranspiration data for the calibration of hydrological modeling in this region, in order to provide some instruction for modelling. Both the streamflow and evapotranspiration simulation were improved. Sensitive parameters were found to be different from calibration using streamflow data, which is one important reason of the improvement.

Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.

The reservoirs of cascade hydropower stations in Hongshuihe basin are facing more and more integrated water resources utilization problem. This paper introduces the integrated water demand of cascade reservoirs in Hongshuihe and analyses the impact of various types of integrated water demand on power generation. It provides a technical and management guide and demonstration for cascade reservoirs operation and basin integrated water management.

The possibility of using a short period of streamflow data (less than one year) to calibrate a physically based distributed hydrological model is evaluated. Contrary to the common understanding of using data of several years, it is shown that only using data covering several months could calibrate the model effectively, which indicates that this approach is valuable for solving the calibration problem of such models in data-sparse basins.