Advances in Earth Science
• Articles •
LIU Chun-zhen. The Advances in Studying Detection of Streamflow Trend Influenced by Climate Change[J]. Advances in Earth Science, 2007, 22(8): 777-783.
Detection of streanflow trend and water resources assessment based on the natural and stationary climatic condition have been challenged by global warming. Identifying and separating contribution of climate change in streamflow trend, facilitate not only in water management practice and water project construction, but also enable understanding where, when and by which way the impact of climate change on hydrological cycle becomes detectable or not yet.The statistical test is a powerful tool for the detection of hydrological trend. It is necessary to consider both the serial correlation of the data series and the cross-correlation between the hydrological variables at different locations for correctly determining the significance level. A complete study of the detection of streamflow trend includes description of trend characteristics and its attribution as well. For natural river basin fed by snow and glaciers melt, the streamflow trend is determined mainly by temperature variation, associated with natural climate variability and external forcing produced climate warming. For managed water systems, supplied by precipitation, streamflow trend is affected not only by climatic variables, but also by anthropogenic disturbances-direct and indirect factors. The methodology of statistical interrelation of multifactors is helpful to determining contribution of each element in streamflow trend.However, by only relying on statistical method alone, it is hard to weigh the interaction among factors as well as to identify the impact of climatic variability and forced climate change on streamflow. The reliability of simulation and projection on future streamflow with climate model directly is directly determined by the ability of the climate model to simulate precipitation of current time-horizon. The ensemble mean from a subset of climate models could reduce uncertainties in precipitation and runoff simulations. Recently developed methodology of ensemble mean of multiclimate models combined with statistical analysis has been used to show the potential in simulation and projection of spatial pattern of macro-scale streamflow trend caused by forced climate change. In the course of improving climate model and regional climate models' simulation of precipitation in particular, it is anticipated that the detection, attribution and projection of streamflow alteration tend to be simulated in an identical way in the near future.