%0 Journal Article
%T 基于空间马尔可夫模型的流域非点源优先管理区识别
Identification of Non-Point Source Priority Management Areas in Watershed Based on Spatial Markov Model
%A 秦俏华
%A 王小胜
%A 侯越
%A 李慧芩
%A 王帅
%A 王明哲
%A 贾茂平
%A 庞树江
%J Advances in Environmental Protection
%P 321-336
%@ 2164-5493
%D 2025
%I Hans Publishing
%R 10.12677/aep.2025.153040
%X 非点源污染是导致地表和地下水环境恶化的重要原因之一。优先管理区(Priority Management Areas, PMAs)识别对非点源污染精准高效管控至关重要。然而，在已有PMAs识别研究中很少考虑河流系统中的传输损失，且缺乏对识别结果准确性和合理性的验证。基于此，本文通过耦合SWAT模型与空间Markov算法，构建了一个考虑河流滞留效应和削减负荷的多级PMAs识别框架，应用于海河水系的王快水库流域，并验证PMAs识别结果在不同管理措施下的准确性。主要结果如下：(1) 在校准和验证期SWAT模型性能良好，纳什效率系数NSE > 0.60、R2 > 0.79；(2) 河道氮磷滞留系数与径流量呈非线性关系，存在显著的径流阈值。当径流量介于1.14~8.23 m3/s，总氮(TN)和总磷(TP)的滞留系数趋于稳定。(3) TN的PMAs均为#29和#30号子流域，负荷贡献为74.35% (枯水年)、58.45% (平水年)和76.46% (丰水年)，面积占流域总面积的14.22%。(4) TP的PMAs为#33 (枯水年)、#28和#33 (平水年)、#29和#30 (丰水年)号子流域，负荷贡献分别达到53.19%、78.83%和69.69%，面积占1.20%、2.95%和14.22%。(5) 仿真结果显示，PMAs削减效率约为非PMAs的4.25倍(TN)和5.37倍(TP)，证实了PMAs识别结果的合理性。该研究结果可为相关流域的非点源污染控制提供有效方法支撑。
Non-point source (NPS) pollution is one of the important contributors to the deterioration of the surface and groundwater environment. The identification of priority management areas (PMAs) is crucial for effective management of NPS pollution. However, transport losses within river ecosystems are seldom considered when identifying PMAs, and there is a lack of validation for the accuracy of the results. Therefore, a framework for multi-level PMAs identification was conducted, considering the effect of river retention and retention load by integrating the SWAT model with the Markov algorithm. This framework is applied to the Wangkuai Reservoir Watershed in the Haihe River system, and the accuracy of the PMAs identification results under different best management measures (BMPs) is verified. The main results are illustrated as follows: (1) The SWAT model showed strong performance with high statistical indicators (NSE > 0.60, R2 > 0.79) for the calibration and validation periods. (2) A non-linear relationship between the retention coefficient and runoff with a significant runoff threshold was detected. For both total nitrogen (TN) and total phosphorus (TP), the retention coefficients held steady when runoff surpassed 1.14~8.23 m3/s. (3) The PMAs for TN are sub-watersheds #29 and #30, with load contributions of 74.35% (dry year), 58.45% (normal year), and 76.46% (wet year), and the area accounts for 14.22% of the total basin area. (4) The PMAs for TP are sub-watershed #33 (dry year), sub-watershed #28 and sub-watershed #33 (normal year), and sub-watershed #29 and #30 (wet year), with load contributions reaching 53.19%, 78.83%, and 69.69% respectively, and the areas account for 1.20%, 2.95%, and 14.22% respectively. (5) The reduction efficiency of PMAs
%K 非点源污染，
%K 优先管理区，
%K SWAT模型，
%K 空间马尔可夫算法，
%K 最佳管理措施
Non-Point Source Pollution
%K Priority Management Areas
%K SWAT Model
%K Spatial Markov Algorithm
%K Best Management Practices
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=109975