基于径流路径的分布式面源污染模型研发与应用进展

Progress on the development and application of a runoff pathway-based Spatially and Temporally distributed Model for Non-Point Source pollution

精准刻画地表径流的路径及其所携带的面源污染物随径流的输移过程是准确估算面源污染入水体量、污染关键源区辨识和高效防控的关键,在我国以小农户种植为主、景观特征复杂的地理条件下尤为重要。鉴于目前常用的面源污染模型大都起源于国外,往往对径流路径的空间差异性及污染物陆面输移过程进行概化,介绍了一个基于径流路径的分布式面源污染模型(STEM-NPS)及其研发与应用进展。阐述了该模型的研发背景、模型原理和结构,说明了STEM-NPS模型对地表径流汇流及其所携带的污染物输移过程的精细化表达方法;介绍了该模型在不同地理环境及尺度的应用进展,展示了其在地块尺度的面源污染关键源区辨识,关键过程和关键影响因素解析及面源污染监管决策支持等方面的功能;探讨了STEM-NPS模型与其他常用模型的异同,并结合生态学研究和面源污染精准防控的需求,提出模型的应用前景及进一步发展的方向。

Non-point source pollution models are useful tools to simulate pollution loads entering water bodies, identify critical source areas and assess best management practices. The accuracy and the reliability of the above information generated by models is highly determined by how a model delineates surface runoff pathways and how it simulates pollutant transport processes along the runoff pathways, which is especially important in complex watersheds with fragment landscapes. Agriculture in China is characterized with small and various types of fields in limited areas, which means fragment landscapes are common. Currently, widely used non-point source models are most developed in western countries, which usually ignore the heterogeneity of fine-scale runoff pathways and simulate terrestrial pollutant transport within a sub-watershed by a lumped approach. This paper presented a Spatially and Temporally distributed Model for Non-Point Source pollution(STEM-NPS) that simulates pollutant transport processes based on fine-scale runoff pathways. First, the background, development process and model theory of STEM-NPS was described. STEM-NPS was first published in international journals in 2017 while the development and modifications continue till right now. The model simulates runoff, nutrient loads and water quality on a grid cell basis and a daily time step. The hydrological module of STEM-NPS is the Distributed Hydrological Model for Watershed Mangement(DHM-WM) which delineates runoff pathways, simulate surface, subsurface and base flow and calculates runoff travel time along pathways. DHM-WM has two water balance routines: local routine is for watersheds dominated by infiltration-excess runoff while global routine is for watersheds with both infiltration-excess and saturation-excess runoff. The pollutant transport module of STEM-NPS simulates nutrient loads generated from source areas and loads entering water bodies with zero-order mobilization and first-order delivery functions respectively. Then, model application cases in different geographic regions and scales were presented. One case was in a plateau watershed in Yunnan province and the other case was in a plain county in Henan province. The application cases showed the model functions in tracking fine-scale runoff pathways, identifying fine-scale critical source areas, analyzing key processes and driving factors of pollutant losses, as well as how the model was used in an on-line support-decision system of non-point source pollution monitoring and control. After that, STEM-NPS was compared with some commonly used models such as SWAT, APEX, HSPF, AnnAGNPS, L-THIA, STEPL-WEB, CADA-ECM and SPARROW, specifically for their functions in identifying critical source areas and assessing best management practices(BMPs). Based on the comparison, application perspectives of STEM-NPS in ecology were proposed, such as assessing the impact of hydrologic connectivity on nutrient transport and analyzing social-economic development impact on water environment. Although with advantage in supporting fine-scale non-point source pollution control, the STEM-NPS model still needs further modifications, such as representing nutrient legacy effect in the nutrient transport module and developing a user-friendly BMP assessment module.