Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China.
J Environ Manage. 2023 Aug 1;339:117846. doi: 10.1016/j.jenvman.2023.117846. Epub 2023 Apr 11.
Quantifying phosphorus (P) load from watersheds at a fine scale is crucial for studying P sources in lake or river ecosystems; however, it is particularly challenging for mountain-lowland mixed watersheds. To address this challenge, we proposed a framework to estimate the P load at the grid scale and assessed its risk to surrounding rivers in a typical mountain-lowland mixed watershed (Huxi Region in Lake Taihu Basin, China). The framework coupled three models: the Phosphorus Dynamic model for lowland Polder systems (PDP), the Soil and Water Assessment Tool (SWAT), and the Export Coefficient Model (ECM). The coupled model performed satisfactory for both hydrological and water quality variables (Nash-Sutcliffe efficiency >0.5). Our modelling practice revealed that polder, non-polder, and mountainous areas had P load of 211.4, 437.2, and 149.9 t yr, respectively. P load intensity in lowlands and mountains was 1.75 and 0.60 kg ha yr, respectively. A higher P load intensity (>3 kg ha yr) was mainly observed in the non-polder area. In lowland areas, irrigated cropland, aquaculture ponds and impervious surfaces contributed 36.7%, 24.8%, and 25.8% of the P load, respectively. In mountainous areas, irrigated croplands, aquaculture ponds, and impervious surfaces contributed 28.6%, 27.0%, and 16.4% of the P load, respectively. Rivers with relatively high P load risks were mainly observed around big cities during rice season, owing to a large contribution of P load from the non-point source pollution of urban and agricultural activities. This study demonstrated a raster-based estimation of watershed P load and their impacts on surrounding rivers using coupled process-based models. It would be useful to identify the hotspots and hot moments of P load at the grid scale.
量化流域的磷（P）负荷对于研究湖泊或河流生态系统中的 P 来源至关重要；然而，对于山地-平原混合流域来说，这尤其具有挑战性。为了解决这一挑战，我们提出了一种在网格尺度上估算 P 负荷的框架，并评估了其对太湖流域典型山地-平原混合流域（湖西地区）周围河流的风险。该框架结合了三个模型：低地圩区磷动态模型（PDP）、土壤和水评估工具（SWAT）和输出系数模型（ECM）。耦合模型在水文和水质变量方面表现出令人满意的性能（纳什-苏特克里夫效率>0.5）。我们的建模实践表明，圩区、非圩区和山区的 P 负荷分别为 211.4、437.2 和 149.9 t yr。低地和山区的 P 负荷强度分别为 1.75 和 0.60 kg ha yr。非圩区主要观察到较高的 P 负荷强度（>3 kg ha yr）。在低地地区，灌溉农田、水产养殖池塘和不透水面分别贡献了 P 负荷的 36.7%、24.8%和 25.8%。在山区，灌溉农田、水产养殖池塘和不透水面分别贡献了 P 负荷的 28.6%、27.0%和 16.4%。在水稻季节，由于城市和农业活动的非点源污染对 P 负荷的大量贡献，高 P 负荷风险的河流主要分布在大城市周围。本研究利用基于栅格的方法，利用耦合的基于过程的模型来估算流域 P 负荷及其对周围河流的影响。这对于确定网格尺度上 P 负荷的热点和热点时刻将非常有用。
