The North China Plain is one of the main grain producing areas in China. However, overexploitation has long been unsustainable since the water supply is mainly from groundwater. Since 2014,the South-to-North Water Div...The North China Plain is one of the main grain producing areas in China. However, overexploitation has long been unsustainable since the water supply is mainly from groundwater. Since 2014,the South-to-North Water Diversion Project's central route has been charted to the integrated management of water supply and over-exploitation, which has alleviated the problem to a certain extent. Although the Ministry of Water Resources has made many efforts on groundwater recharge since 2018 most of which have been successful, the recharge has not yet been sufficiently focused on the repair of shallow groundwater depression zones. It still needs further optimization. This paper discusses this particular issue,proposes optimized recharge plan and provides the following recommendations:(1) Seven priority target areas are selected for groundwater recharge in alluvial and proluvial fans in the piedmont plain, and the storage capacity is estimated to be 181.00×10~8 m~3;(2) A recharge of 31.18×10~8 m~3/a is required by 2035 to achieve the repair target;(3) It is proposed to increase the recharge of Hutuo River, Dasha River and Tanghe River to 19.00×10~8 m~3/a and to rehabilitate Gaoliqing-Ningbailong Depression Zone;increase the recharge of Fuyang River, Zhanghe River and Anyang River to 7.05×10~8 m~3/a and rehabilitate Handan Feixiang-Guangping Depression Zone;increase the recharge of Luanhe River by 0.56×10~8 m~3/a and restore Tanghai Depression Zone and Luanan-Leting Depression Zone;moderately reduce the amount of water recharged to North Canal and Yongding River to prevent excessive rebound of groundwater;(4) Recharge through well is implemented on a pilot basis in areas of severe urban ground subsidence and coastal saltwater intrusion;(5) An early warning mechanism for groundwater quality risks in recharge areas is established to ensure the safety. The numerical groundwater flow model also proves reasonable groundwater level restoration in the depression zones by 2035.展开更多
Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal...Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.展开更多
为了获取不同农作物的空间分布信息,以华北平原黄河以北地区为研究区域,利用Savitzky-Golay滤波对2014—2016年的时间序列叶面积指数(leaf area index,LAI)进行重构,进而应用一阶差分法和重构LAI的傅里叶变换的谐波特征对研究区域主要...为了获取不同农作物的空间分布信息,以华北平原黄河以北地区为研究区域,利用Savitzky-Golay滤波对2014—2016年的时间序列叶面积指数(leaf area index,LAI)进行重构,进而应用一阶差分法和重构LAI的傅里叶变换的谐波特征对研究区域主要农作物冬小麦、玉米和棉花种植区域进行识别和提取,并对不同作物的识别精度进行验证。结果表明,基于Savitzky-Golay滤波重构的LAI能够去除由云、大气等因素造成的LAI骤降的影响,重构LAI曲线平滑且符合作物的生长规律特征。研究区域2014—2016年作物识别的总体精度均大于80.00%,2015年达到87.08%,冬小麦-夏玉米、春玉米、棉花和单季夏玉米的识别精度分别为92.50%、80.00%、85.00%和82.50%,表明利用一阶差分法能够准确提取研究区域一年一季和一年两季作物种植区域。结合傅里叶变换方法和作物物候信息能够有效地识别不同作物的种植区域,进而获取研究区域主要农作物的分布信息。该研究可为研究区域主要作物的长势监测及产量估测预测提供参考。展开更多
基金funded by Geological Joint Fund of the National Natural Science Foundation of China (U2244214)China Geological Survey Program (DD20190336, DD20221752, DD20230078)+1 种基金Chinese Academy of Geological Sciences Basic Research Fund Program (SK202118, SK202216)Hebei Provincial Innovation Capacity Enhancement Program for High-level Talent Team Building (225A4204D)。
文摘The North China Plain is one of the main grain producing areas in China. However, overexploitation has long been unsustainable since the water supply is mainly from groundwater. Since 2014,the South-to-North Water Diversion Project's central route has been charted to the integrated management of water supply and over-exploitation, which has alleviated the problem to a certain extent. Although the Ministry of Water Resources has made many efforts on groundwater recharge since 2018 most of which have been successful, the recharge has not yet been sufficiently focused on the repair of shallow groundwater depression zones. It still needs further optimization. This paper discusses this particular issue,proposes optimized recharge plan and provides the following recommendations:(1) Seven priority target areas are selected for groundwater recharge in alluvial and proluvial fans in the piedmont plain, and the storage capacity is estimated to be 181.00×10~8 m~3;(2) A recharge of 31.18×10~8 m~3/a is required by 2035 to achieve the repair target;(3) It is proposed to increase the recharge of Hutuo River, Dasha River and Tanghe River to 19.00×10~8 m~3/a and to rehabilitate Gaoliqing-Ningbailong Depression Zone;increase the recharge of Fuyang River, Zhanghe River and Anyang River to 7.05×10~8 m~3/a and rehabilitate Handan Feixiang-Guangping Depression Zone;increase the recharge of Luanhe River by 0.56×10~8 m~3/a and restore Tanghai Depression Zone and Luanan-Leting Depression Zone;moderately reduce the amount of water recharged to North Canal and Yongding River to prevent excessive rebound of groundwater;(4) Recharge through well is implemented on a pilot basis in areas of severe urban ground subsidence and coastal saltwater intrusion;(5) An early warning mechanism for groundwater quality risks in recharge areas is established to ensure the safety. The numerical groundwater flow model also proves reasonable groundwater level restoration in the depression zones by 2035.
文摘Leaf area index （LAI） is an important parameter in a number of models related to ecosystem functioning, carbon budgets, climate, hydrology, and crop growth simulation. Mapping and monitoring the spatial and temporal variations of LAI are necessary for understanding crop growth and development at regional level. In this study, the relationships between LAI of winter wheat and Landsat TM spectral vegetation indices （SVIs） were analyzed by using the curve estimation procedure in North China Plain. The series of LAI maps retrieved by the best regression model were used to assess the spatial and temporal variations of winter wheat LAI. The results indicated that the general relationships between LAI and SVIs were curvilinear, and that the exponential model gave a better fit than the linear model or other nonlinear models for most SVIs. The best regression model was constructed using an exponential model between surface-reflectance-derived difference vegetation index （DVI） and LAI, with the adjusted R2 （0.82） and the RMSE （0.77）. The TM LAI maps retrieved from DVILAI model showed the significant spatial and temporal variations. The mean TM LAI value （30 m） for winter wheat of the study area increased from 1.29 （March 7, 2004） to 3.43 （April 8, 2004）, with standard deviations of 0.22 and 1.17, respectively. In conclusion, spectral vegetation indices from multi-temporal Landsat TM images can be used to produce fine-resolution LAI maps for winter wheat in North China Plain.
文摘为了获取不同农作物的空间分布信息,以华北平原黄河以北地区为研究区域,利用Savitzky-Golay滤波对2014—2016年的时间序列叶面积指数(leaf area index,LAI)进行重构,进而应用一阶差分法和重构LAI的傅里叶变换的谐波特征对研究区域主要农作物冬小麦、玉米和棉花种植区域进行识别和提取,并对不同作物的识别精度进行验证。结果表明,基于Savitzky-Golay滤波重构的LAI能够去除由云、大气等因素造成的LAI骤降的影响,重构LAI曲线平滑且符合作物的生长规律特征。研究区域2014—2016年作物识别的总体精度均大于80.00%,2015年达到87.08%,冬小麦-夏玉米、春玉米、棉花和单季夏玉米的识别精度分别为92.50%、80.00%、85.00%和82.50%,表明利用一阶差分法能够准确提取研究区域一年一季和一年两季作物种植区域。结合傅里叶变换方法和作物物候信息能够有效地识别不同作物的种植区域,进而获取研究区域主要农作物的分布信息。该研究可为研究区域主要作物的长势监测及产量估测预测提供参考。
