The forecast of soil moisture lays foundation for water management in farmlands. The change of soil moisture is influenced by multiple meteorological fac- tors. It becomes much significant for improvement of agricultu...The forecast of soil moisture lays foundation for water management in farmlands. The change of soil moisture is influenced by multiple meteorological fac- tors. It becomes much significant for improvement of agricultural production and ef- fective use of water to explore the rule of water dynamic at small scale, spatially or temporally. In the research, water dynamic in soil horizons at 0-40 cm in winter wheat belts was simulated by SIMPLE model as per water balance principle. Fur- thermore, ETp in fields was computed according to Haude method （DVWK stan- dards）; retained amount of water in fields and wilting coefficient were calculated based on soil parameters with SPAW （Soil-Plant-Air-Water）. The simulated results of SIMPLE model showed that the correlation of measured and simulated water con- tent in soils was 0.95 and relative error averaged lower than 3.1%, suggesting that the model would make a more precise estimation of water content in root zone in the area.展开更多
文摘The forecast of soil moisture lays foundation for water management in farmlands. The change of soil moisture is influenced by multiple meteorological fac- tors. It becomes much significant for improvement of agricultural production and ef- fective use of water to explore the rule of water dynamic at small scale, spatially or temporally. In the research, water dynamic in soil horizons at 0-40 cm in winter wheat belts was simulated by SIMPLE model as per water balance principle. Fur- thermore, ETp in fields was computed according to Haude method （DVWK stan- dards）; retained amount of water in fields and wilting coefficient were calculated based on soil parameters with SPAW （Soil-Plant-Air-Water）. The simulated results of SIMPLE model showed that the correlation of measured and simulated water con- tent in soils was 0.95 and relative error averaged lower than 3.1%, suggesting that the model would make a more precise estimation of water content in root zone in the area.
