Integrating land use type and other geographic information within spatial interpolation has been proposed as a solution to improve the performance and accuracy of soil nutrient mapping at the regional scale. This stud...Integrating land use type and other geographic information within spatial interpolation has been proposed as a solution to improve the performance and accuracy of soil nutrient mapping at the regional scale. This study developed a non-algorithm approach, i.e., applying inverse distance weighting （IDW） and ordinary kriging （OK）, to individual land use types rather than to the whole watershed, to determine if this improved the performance in mapping soil total C （TC）, total N （TN）, and total P （TP） in a 200-km2 urbanizing watershed in Southeast China. Four land use types were identified by visual interpretation as forest land, agricultural land, green land, and urban land. One hundred and fifty soil samples （0-10 cm） were taken according to land use type and patch size. Results showed that the non-algorithm approach, interpolation based on individual land use types, substantially improved the performance of IDW and OK for mapping TC, TN, and TP in the watershed. Root mean square errors were reduced by 3.9% for TC, 10.770 for TN, and 25.9% for TP by the application of IDW, while the improvements by OK were slightly lower as 0.9% for TC, 7.7% for TN, and 18.1% for TP. Interpolations based on individual land use types visually improved depiction of spatial patterns for TC, TN, and TP in the watershed relative to interpolations by the whole watershed. Substantial improvements might be expected with denser sampling points. We suggest that this non-algorithm approach might provide an alternative to algorithm-based approaches to depict watershed-scale nutrient patterns.展开更多
考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度...考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。展开更多
基金supported by the Knowledge Innovation Program of Chinese Academy of Sciences(No.KZCX2-YWJC402)the Hundred Talents Program of Chinese Academy of Sciences(No.A0815)+1 种基金the National Natural Science Foundation of China(No.41371474)supported by the Chinese Academy of Sciences Visiting Professorships for Senior International Scientists in 2011(No.2011T2Z18)
文摘Integrating land use type and other geographic information within spatial interpolation has been proposed as a solution to improve the performance and accuracy of soil nutrient mapping at the regional scale. This study developed a non-algorithm approach, i.e., applying inverse distance weighting （IDW） and ordinary kriging （OK）, to individual land use types rather than to the whole watershed, to determine if this improved the performance in mapping soil total C （TC）, total N （TN）, and total P （TP） in a 200-km2 urbanizing watershed in Southeast China. Four land use types were identified by visual interpretation as forest land, agricultural land, green land, and urban land. One hundred and fifty soil samples （0-10 cm） were taken according to land use type and patch size. Results showed that the non-algorithm approach, interpolation based on individual land use types, substantially improved the performance of IDW and OK for mapping TC, TN, and TP in the watershed. Root mean square errors were reduced by 3.9% for TC, 10.770 for TN, and 25.9% for TP by the application of IDW, while the improvements by OK were slightly lower as 0.9% for TC, 7.7% for TN, and 18.1% for TP. Interpolations based on individual land use types visually improved depiction of spatial patterns for TC, TN, and TP in the watershed relative to interpolations by the whole watershed. Substantial improvements might be expected with denser sampling points. We suggest that this non-algorithm approach might provide an alternative to algorithm-based approaches to depict watershed-scale nutrient patterns.
文摘考虑到当前梯级水库蓄水调度研究尚未开展碳减排调度,基于碳排放因子法提出了梯级水库蓄水期水碳多目标调度模型,制定了梯级水库提前蓄水策略,并以防洪风险最小化、发电量最大化和温室气体排放量最小化为调度目标,采用NSGA-II求解调度模型推求了梯级水库蓄水期优化调度方案,在金沙江中下游6座水库与三峡水库组成的梯级水库开展了实例研究。结果表明:相较于现行调度方案,优化调度方案集在防洪库容占用率为0~4.92%的情况下,发电量提升了7.23~40.26亿kW·h/a(0.65%~3.60%),弃水量减少了15.82~55.03亿m^(3)/a(6.45%~22.43%),温室气体排放量降低了38.55~45.63 Gg CO_(2e)/a(8.33%~9.85%),碳排放强度降低了0.39~0.47 kg CO_(2e)/(MW·h)(9.49%~11.44%),显著提升了梯级水库的发电量、抗旱供水能力并减少了温室气体排放。研究成果为实现梯级水库蓄水期水碳协同调度提供了技术支撑。
