高级检索+

中国北方耕地水土平衡关系时空特征分析

Analysis of spatio-temporal characteristics of water and cultivated land resources balance in Northern China

  • 摘要: 耕地水土平衡关系是维持耕地生产力、生态稳定性与可持续利用能力的基础支撑和核心调控机制,研究耕地水土平衡关系对于支撑“以水定地”,促进耕地空间布局优化、作物种植结构优化和水土资源可持续利用具有重要现实意义。本文以我国北方五大农业区为研究区,基于2001—2022年逐月栅格气象数据、作物种植结构与总初级生产力(Gross Primary Productivity, GPP)数据,采用FAO-56作物系数法估算冬小麦、春玉米和夏玉米全生育期作物需水量,构建旬尺度作物水分盈亏指数(Crop Water Deficit&Surplus Index, CWDI)及其标准化指数(Standardized Crop Water Deficit&Surplus Index,,SCWDI),并结合游程理论、经验正交函数(Empirical Orthogonal Function, EOF)、相关分析和Copula–Bayesian条件概率模型,系统识别北方耕地水土平衡关系的时空演变、作物需水临界期及干旱胁迫下的生产力损失风险。结果表明:1)研究区冬小麦与玉米全生育期多年均值需水分别为500 mm与594 mm,其中春玉米为627 mm、夏玉米为536 mm;需水量呈显著区域差异,冬小麦:黄淮海区521 mm>黄土高原区514 mm>甘新区467 mm,而春玉米在甘新区最高(662 mm)2)研究区平均干旱事件频率为0.4—2.0次/a,甘新区表现出特旱占比较高、历时较长、强度较大的累积型干旱特征,黄淮海区则以高频、短历时干旱为主。3)EOF分解显示前5个模态累计解释73.3%的方差,EOF1和EOF2贡献率分别为28.6%和20.0%,表明北方耕地干湿变化在年际尺度上同时具有全区一致性和区域差异性特征。4)SCWDI与SGPP相关分析表明,春玉米和夏玉米识别的关键月份为7月、冬小麦为5月,考虑累积效应后,春玉米、夏玉米和冬小麦的关键需水阶段分别对应5—6月、6月和3—4月。 5)春玉米与夏玉米的SCWDI—SGPP以Gaussian Copula拟合效果最佳,冬小麦以Frank Copula最优;特旱条件下春玉米、夏玉米和冬小麦的平均生产力损失概率分别约为63%、80%和44%,黄淮海区北中部及黄土高原区是干旱胁迫向生产力损失转化较为敏感的重点区域。本文构建了作物需水—水分盈亏—干旱事件—生产力损失风险的耦合分析框架,可为北方地区耕地布局优化、作物结构调整和农业干旱风险防控提供依据。

     

    Abstract: The cropland water–land balance relationship is a fundamental support and key regulatory mechanism for maintaining cropland productivity, ecological stability, and sustainable land use. Investigating this relationship is of great practical significance for supporting the principle of “determining land use by water availability”, optimizing cropland spatial layout and crop planting structure, and promoting the sustainable utilization of water and land resources. Taking the five major agricultural regions in northern China as the study area, this study used monthly gridded meteorological data, crop planting structure data, and gross primary productivity (GPP) data from 2001 to 2022. The full-growing-season crop water requirements of winter wheat, spring maize, and summer maize were estimated using the FAO-56 crop coefficient method. A dekadal Crop Water Deficit and Surplus Index (CWDI) and its standardized form, the Standardized Crop Water Deficit and Surplus Index (SCWDI), were then constructed. By integrating run theory, empirical orthogonal function (EOF) analysis, correlation analysis, and a Copula–Bayesian conditional probability model, this study systematically identified the spatiotemporal evolution of cropland water–land balance relationships, crop critical water-demand periods, and productivity loss risks under drought stress in northern China.The results showed that: (1) The multi-year mean full-growing-season water requirements of winter wheat and maize in the study area were 500 mm and 594 mm, respectively, with 627 mm for spring maize and 536 mm for summer maize. Crop water requirements showed significant regional differences. For winter wheat, the water requirement followed the order Huang–Huai–Hai Plain (521 mm) > Loess Plateau (514 mm) > Gansu–Xinjiang Region (467 mm), while spring maize had the highest water requirement in the Gansu–Xinjiang Region (662 mm). (2) The average drought-event frequency in the study area ranged from 0.4 to 2.0 events per year. The Gansu–Xinjiang Region was characterized by a high proportion of extreme drought events, long duration, and high intensity, indicating a cumulative drought pattern, whereas the Huang–Huai–Hai Plain was dominated by high-frequency but short-duration drought events. (3) EOF decomposition showed that the first five modes cumulatively explained 73.3% of the total variance, with EOF1 and EOF2 accounting for 28.6% and 20.0%, respectively, indicating that interannual dry–wet variations of cropland in northern China exhibited both region-wide consistency and regional heterogeneity. (4) Correlation analysis between SCWDI and standardized GPP (SGPP) showed that the identified key months were July for spring maize and summer maize, and May for winter wheat. After considering the cumulative effect, the critical water-demand stages of spring maize, summer maize, and winter wheat corresponded to May–June, June, and March–April, respectively. (5) The SCWDI–SGPP dependence structures of spring maize and summer maize were best fitted by the Gaussian Copula, while the Frank Copula performed best for winter wheat. Under extreme drought conditions, the average probabilities of productivity loss for spring maize, summer maize, and winter wheat were approximately 63%, 80%, and 44%, respectively. The north-central Huang–Huai–Hai Plain and the Loess Plateau were identified as key regions where drought stress was more likely to be transformed into productivity loss. This study develops an integrated analytical framework linking crop water requirement, water deficit and surplus, drought events, and productivity loss risk, providing a scientific basis for cropland layout optimization, crop structure adjustment, and agricultural drought risk prevention and control in northern China.

     

/

返回文章
返回