Abstract:

Waterlogging is one of the most common meteorological injuries for winter wheat in the Yangtze-Huai Plain, China. Waterlogging not only reduces wheat root activity, photosynthesis and nutrient metabolism when it occurs during the critical growth stages, but also makes it easy for wheat to be damaged by weed, insects and diseases.
In recent 20 years, with global climate warming, the temperature during wheat growing season has significantly increased in the studied region, in company with reduction of sunshine duration. The combined effect has enhanced waterlogging and often results in reduction of wheat yield.
An early warning system to predict waterlogging injuries for winter wheat (WWWS) was established in this study, aiming at improving meteorological service for local wheat production.
The WWWS consists of two wheat models, i.e., models with and without waterlogging consideration. The latter was rebuilt based on the Wheat Cultivational Simulation, Optimization and Decision- Making System (WCSODS) developed by the authors, i.e., cutting off some optimization models and adding to the submodels of dry matter partitioning, root dry matter increase and leaf senescence in late period, as well as linking directly the LAI dynamics with dry matter production. The former was constructed by increasing several submodels to the wheat model without considering waterlogging, which described the effects of waterlogging on wheat photosynthesis, dry matter partitioning and leaf senescence, respectively. After running these two models under same meteorological condition, the yield difference (ΔY) simulated would give the degree of waterlogging for wheat, i.e., the lager the ΔY, the severer the waterlogging for wheat, and vice versa. In other words, ΔY serves as a criterion of the WWWS to predict if wheat waterlogging occurs or not.
The results of sensitivity analyses for the model considering waterlogging showed that wheat responded more slightly to waterlogging in early stage (tillering and elongation) than in late stage (booting and filling) and the yield loss percentage increased with increasing of waterlogging duration in days. These results are coincident with the knowledge of local wheat experts, indicting that the model with waterlogging consideration is rather reasonable.
The methodologies in development of the WWWS for early warning purpose were also discussed, including building soil moisture submodel to estimate the waterlogging coefficient (fSW), constructing the future weather database files during wheat growing period, regulating and determining the regional parameters of wheat model using the long-term yield data after deducting the part of yield increase due to scientific and technological progress, establishing the indices for warning waterlogging injuries and connection of the WWWS with the Regional Global Climate Change Model (RGCM) etc. Finally, the WWWS was validated using the historical data (1991–2000) both of climates and wheat yields in Nanjing and Nantong areas. The results showed a good prediction for water- logging injuries with 90% correctness. Testing forecasts were also made to predict wheat waterlogging injuries in recent 3 years (2002–2005) using the WWWS and RGCM output in Nanjing, Hefei and Nantong areas and the results showed that predictions in two years were correct and a supplement forecast also gave a correct prediction in another year.
In summary, the WWWS can be used as a useful tool to predict waterlogging injuries for winter wheat in the Yangtze-Huai Plain.

Key words: Yangtze-Huai Plain, Winter wheat, Waterlogging injury, Simulation model, Early warning system