Drip-irrigation is increasingly applied in maize （Zea mays L.） production in sub-humid region. It is cdtical to quantify irrigation requirements during different growth stages under diverse climatic conditions. In t...Drip-irrigation is increasingly applied in maize （Zea mays L.） production in sub-humid region. It is cdtical to quantify irrigation requirements during different growth stages under diverse climatic conditions. In this study, the Hybrid-Maize model was calibrated and applied in a sub-humid Heilongjiang Province in Northeast China to estimate irrigation requirements for drip- irrigated maize during different crop physiological development stages and under diverse agro-climatic conditions. Using dimensionless scales, the whole growing season of maize was divided into diverse development stages from planting to maturity. Drip-irrigation dates and irrigation amounts in each irrigation event were simulated and summarized in 30-year simulation from 1981 to 2010. The maize harvest area of Heilongjiang Province was divided into 10 agro-climatic zones based on growing degree days, arid index, and temperature seasonality. The simulated results indicated that seasonal irrigation requirements and water stress during different growth stages were highly related to initial soil water content and distribution of seasonal precipitation. In the experimental site, the average irrigation amounts and times ranged from 48 to 150 mm with initial soil water content decreasing from 100 to 20% of the maximum soil available water. Additionally, the earliest drip-irrigation event might occur during 3- to 8-leaf stage. The water stress could occur at any growth stages of maize, even in wet years with abundant total seasonal rainfall but poor distribution. And over 50% of grain yield loss could be caused by extended water stress during the kernel setting window and grain filling period. It is estimated that more than 94% of the maize harvested area in Heilongjiang Province needs to be irrigated although the yield increase varied （0 to 109%） in diverse agro-climatic zones. Consequently, at least 14% of more maize production could be achieved through drip-irrigation systems in Heilongjiang Province compared to rainfed conditions.展开更多
Regional runoff prediction plays a crucial role in enhancing water resource management and ensuring water security in ungauged basins.This study selects 40 typical watersheds in major river systems across China.We eva...Regional runoff prediction plays a crucial role in enhancing water resource management and ensuring water security in ungauged basins.This study selects 40 typical watersheds in major river systems across China.We evaluate the performance of a traditional hydrological model(HMETS)and the Long Short-Term Memory(LSTM)network for locally calibrated modeling and runoff prediction in ungauged basins.Furthermore,we investigate training strategies and optimal approaches for regional LSTM models.Key findings include:(1)Both HMETS and LSTM show similar performance in locally calibrated models,yet both fail to capture runoff generation mechanisms under significant anthropogenic disturbances;(2)Among regional parameterization approaches,the spatial proximity-result averaging method performs well in undisturbed basins,but generally fails in human-dominated basins;(3)LSTM models demonstrate strong capabilities in identifying the underlying relationships between precipitation and runoff,achieving superior prediction accuracy in ungauged basins compared to traditional hydrological methods.The proposed training strategy further enhances cross-basin generalization performance;4)Overall,model performance shows a nonlinear trend,initially increasing and then decreasing,as the number of donor basins increases.This highlights the importance of basin similarity,especially the alignment of human activity patterns between human-dominated basins.These findings provide valuable insights for runoff prediction in ungauged regions across China.展开更多
基金supported by the Key Technology R&D Program of China during the 12th Five-year Plan period (2014BAD12B05)the National Natural Science Foundation of China (51479211, 51621061)the Chinese Scholarship Council (201506350059)
文摘Drip-irrigation is increasingly applied in maize （Zea mays L.） production in sub-humid region. It is cdtical to quantify irrigation requirements during different growth stages under diverse climatic conditions. In this study, the Hybrid-Maize model was calibrated and applied in a sub-humid Heilongjiang Province in Northeast China to estimate irrigation requirements for drip- irrigated maize during different crop physiological development stages and under diverse agro-climatic conditions. Using dimensionless scales, the whole growing season of maize was divided into diverse development stages from planting to maturity. Drip-irrigation dates and irrigation amounts in each irrigation event were simulated and summarized in 30-year simulation from 1981 to 2010. The maize harvest area of Heilongjiang Province was divided into 10 agro-climatic zones based on growing degree days, arid index, and temperature seasonality. The simulated results indicated that seasonal irrigation requirements and water stress during different growth stages were highly related to initial soil water content and distribution of seasonal precipitation. In the experimental site, the average irrigation amounts and times ranged from 48 to 150 mm with initial soil water content decreasing from 100 to 20% of the maximum soil available water. Additionally, the earliest drip-irrigation event might occur during 3- to 8-leaf stage. The water stress could occur at any growth stages of maize, even in wet years with abundant total seasonal rainfall but poor distribution. And over 50% of grain yield loss could be caused by extended water stress during the kernel setting window and grain filling period. It is estimated that more than 94% of the maize harvested area in Heilongjiang Province needs to be irrigated although the yield increase varied （0 to 109%） in diverse agro-climatic zones. Consequently, at least 14% of more maize production could be achieved through drip-irrigation systems in Heilongjiang Province compared to rainfed conditions.
基金supported by the Natural Science Foundation of Hunan Province of China(Grant No.2022JJ40492)the Hunan Provincial Water Science Project(Grant No.XSKJ2024064-19)。
文摘Regional runoff prediction plays a crucial role in enhancing water resource management and ensuring water security in ungauged basins.This study selects 40 typical watersheds in major river systems across China.We evaluate the performance of a traditional hydrological model(HMETS)and the Long Short-Term Memory(LSTM)network for locally calibrated modeling and runoff prediction in ungauged basins.Furthermore,we investigate training strategies and optimal approaches for regional LSTM models.Key findings include:(1)Both HMETS and LSTM show similar performance in locally calibrated models,yet both fail to capture runoff generation mechanisms under significant anthropogenic disturbances;(2)Among regional parameterization approaches,the spatial proximity-result averaging method performs well in undisturbed basins,but generally fails in human-dominated basins;(3)LSTM models demonstrate strong capabilities in identifying the underlying relationships between precipitation and runoff,achieving superior prediction accuracy in ungauged basins compared to traditional hydrological methods.The proposed training strategy further enhances cross-basin generalization performance;4)Overall,model performance shows a nonlinear trend,initially increasing and then decreasing,as the number of donor basins increases.This highlights the importance of basin similarity,especially the alignment of human activity patterns between human-dominated basins.These findings provide valuable insights for runoff prediction in ungauged regions across China.
