Experimental Study and Verification of Non-Point Source Pollution from Paddy Fields with Multiple Runoff Outlets
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摘要: 为验证一种水稻田非点源污染原位观测方法的准确性和适应性,以北京市上庄镇某试验水稻田为例,应用原位观测方法分别在水稻田进水口附近(4#观测点)和出水口附近(2#观测点)安装水位计观测稻田水深,利用同步观测的降雨数据以及田内5个采样点定期采集的水质数据,进行非点源污染试验研究,探究基于同一块水稻田不同位置的水深观测数据是否对该方法的应用产生影响.结果表明:①晒田期间,由于4#观测点处于相对低洼的位置,其水深数据显示4 d的无水期,与实际晒田时间相符,而2#观测点水深数据显示10 d无水.②基于2#、4#观测点水深数据得到总蒸散发和渗漏损失、总径流量以及灌溉量的相对偏差分别为1.3%、1.0%、1.8%;应用原位观测方法估算出该水稻田的灌溉量在2 620~2 710 m3之间.③基于2#、4#观测点水深得到的TN、NH4+-N、NO3--N、TP、CODCr输出系数的相对偏差在0.8%~8.0%之间.研究显示:水位计安装位置对原位观测方法应用的影响不大,且主要体现在晒田期的水深观测,建议将水位计安装在稻田相对低洼处,以确保能够完整地监测水稻生长季的水深变化;应用该方法能准确识别水稻田的径流量和灌溉量,对具有多个径流出口的水稻田非点源污染试验具有较好的准确性和适应性.Abstract: In order to evaluate the accuracy and adaptability of an in-situ observation method for non-point source pollution (NPS) in paddy fields, a paddy field in Shangzhuang Town of Beijing City was chosen for NPS pollution experiments, and two water level gauges were installed near the water inlet (4# observation point) and outlet (2# observation point) of the paddy field to observe the water depth. Combining the rainfall and observed surface water quality data, and using in-situ observation method, the NPS pollution loads in the effluent of the paddy field were calculated. Then explore whether the data of water depth at different locations in the same paddy field have an impact on the application of this method. The results showed that 4# observation point was located at a lower position compared with 2# observation point. According to 4# observation point, there were 4 days of dry field in the drying paddy field period, which was consistent with the actual drying paddy field period, and there was a dry period of 10 days based on 2# observation point. The relative deviation of the evaporation and infiltration loss, runoff, and irrigation based on the two observation points was 1.3%, 1.0% and 1.8% respectively. The in-situ observation method was used to estimate the irrigation amount of the paddy field between 2620 m3 and 2710 m3. The relative deviation of the NPS pollution of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (CODCr), nitrate-nitrogen (NO3--N) and ammonia nitrogen (NH4+-N) was between 0.8% and 8.0%. This indicated that the installation position of the water level gauge has little influence on the application of the in-situ observation method, and it is mainly reflected in the water depth observation during the field drying period. It is recommended to install the water level gauges in low-lying areas of paddy fields, so that the variation of water depth can be monitored throughout the whole rice-growing season. The method can accurately identify the runoff and irrigation volume of paddy fields, and can be applied for accurate and reliable NPS analyses in paddy fields with multiple runoff outlets.
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表 1 基于2#、4#观测点水深的水稻田出流估计
Table 1. Calculation of runoff outflow based on water depth of the observation points 2# and 4#
场次 时间 Pi/mm 2#观测点 4#观测点 径流类型 (Hi+1-Hi)/mm EFi/mm Rout, i/mm (Hi+1-Hi)/mm EFi/mm Rout, i/mm 1 6月6—7日 22.6 -7.1 6.3 17.1 -9.0 6.9 17.8 降雨径流 2 6月13—14日 12.1 -6.2 6.1 6.2 -5.3 7.0 3.4 降雨径流 3 6月20日 5.5 -3.6 5.5 3.6 -3.5 6.6 2.5 降雨径流 4 7月5日 0.0 -15.0 7.9 7.1 -15.3 7.9 8.2 灌溉回归 5 7月16—19日 43.4 -13.8 5.6 9.9 -37.3 6.6 17.4 降雨径流 6 7月24—26日 0.0 -64.5 7.8 41.2 -58.3 7.3 36.4 人工排水 7 7月30—31日 40.0 -42.7 8.9 25.1 -43.6 9.3 25.1 降雨径流 8 8月8日 0.0 -18.2 7.5 10.7 -18.5 8.5 10.0 灌溉回归 9 8月10—12日 22.0 -32.8 7.0 14.6 -33.4 7.5 12.0 降雨径流 10 8月19日 0.0 -14.2 7.5 6.7 -14.1 7.4 6.7 灌溉回归 表 2 水稻田水量收支平衡
Table 2. Water balance based on water depth variation of the observation points 2# and 4#
mm 观测点 P Rin H1 EF Rout Qin Qout 2# 208.3 541.9 70 678 142.2 820.2 820.2 4# 208.3 523.2 104 696 139.5 835.9 835.9 注:H1表示试验水稻田从开始水深观测之前水稻田中的初始水量;Qin表示试验田在开始水深观测到结束观测之间总获得的水量,为P、Rin、H1之和;Qout表示试验水稻田在开始水深观测到结束观测之间总损失的水量,为EF、Rout之和. 表 3 部分水稻田污染物输出系数对比
Table 3. Comparison of pollution export coEFficients of some studied paddy field
年份 研究区 输出系数/(kg/hm2) 数据来源 TN TP NH4+-N NO3--N 2018 上海市奉贤区 4.61 0.44 1.5 0.37 文献[31] 2015 上海市青浦农业园区 23.91 2.67 — — 文献[30] 2012 北京市海淀区上庄镇 8.8 0.47 — — 文献[21] 2011 海河流域 4.77 2.08 — — 文献[33] 2006 韩国索罗流域 3.9 0.77 — — 文献[34] 2014 北京市海淀区上庄镇 6.03 0.37 1.54 0.40 该研究 2#观测点 2014 北京市海淀区上庄镇 7.08 0.39 1.80 0.43 该研究 4#观测点 -
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