As dominant biomes,forests play an important and indispensable role in adjusting the global carbon balance under climate change.Therefore,there are scientific and political implications in investigating the carbon budget of forest ecosystems and its response to climate change.Here we synthesized the most recent research progresses on the carbon cycle in terrestrial ecosystems,and applied an individual-based forest ecosystem carbon budget model for China(FORCCHN) to simulate the dynamics of the carbon fluxes of forest ecosystems in the northeastern China.The FORCCHN model was further improved and applied through adding variables and modules of precipitation(rainfall and snowfall) interception by tree crown,understory plants and litter.The results showed that the optimized FORCCHN model had a good performance in simulating the carbon budget of forest ecosystems in the northeastern China.From 1981 to 2002,the forests played a positive role in absorbing carbon dioxide.However,the capability of forest carbon sequestration had been gradually declining during the the same period.As for the average spatial distri-bution of net carbon budget,a majority of the regions were carbon sinks.Several scattered areas in the Heilongjiang Province and the Liaoning Province were identified as carbon sources.The net carbon budget was apparently more sensitive to an increase of air temperature than change of precipitation.
There is an increasing trend to incorporate the basin hydrological model into the traditional land surface model （LSM） to improve the description of hydrological processes in them. For incorporating with the Noah LSM, a new rainfall-runoff model named XXT （the first X stands for Xinanjiang, the second X stands for hybrid, and T stands for TOPMODEL） was developed and presented in this study, based on the soil moisture storage capacity distribution curve （SMSCC）, some essential modules of the Xinanjiang model, together with the simple model framework of the TOPMODEL （a topography based hydrological model）. The innovation of XXT is that the water table is incorporated into SMSCC and it connects the surface runoff production with base flow production. This improves the description of the dynamically varying saturated areas that produce runoff and also captures the physical underground water level. XXT was tested in a small-scale watershed Youshuijie （946 km2） and a large-scale watershed Yinglouxia （10009 km2） in China. The results show that XXT has better performance against the TOPMODEL and the Xinanjiang model for the two watersheds in both the calibration period and the validation period in terms of the Nash-Sutcliffe efficiency. Moreover, XXT captures the largest peak flow well for both the small： and large-scale watersheds during the validation period, while the TOPMODEL produces significant overestimates or underestimates, so does the Xinanjiang model.
Early and effective flood warning is essential for reducing loss of life and economic damage. Three global ensemble weather prediction systems of the China Meteorological Administration （CMA）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, and the US National Centers for Environmental Prediction （NCEP） in THORPEX （The Observing System Research and Predictability Experiment） In- teractive Grand Global Ensemble （TIGGE） archive are used in this research to drive the Global/Regional Assimilation and PrEdiction System （GRAPES） to produce 6-h lead time forecasts. The output （precipita- tion, air temperature, humidity, and pressure） in turn drives a hydrological model XXT （the first X stands for Xinanjiang, the second X stands for hybrid, and T stands for TOPMODEL）, the hybrid model that combines the TOPMODEL （a topography based hydrological model） and the Xinanjiang model, for a case study of a flood event that lasted from 18 to 20 July 2007 in the Linyi watershed. The results show that rainfall forecasts by GRAPES using TIGGE data from the three forecast centers all underestimate heavy rainfall rates; the rainfall forecast by GRAPES using the data from the NCEP is the closest to the obser- vation while that from the CMA performs the worst. Moreover, the ensemble is not better than individual members for rainfall forecasts. In contrast to corresponding rainfall forecasts, runoff forecasts are much better for all three forecast centers, especially for the NCEP. The results suggest that early flood warning by the GRAPES/XXT model based on TIGGE data is feasible and this provides a new approach to raise preparedness and thus to reduce the socio-economic impact of floods.
