Abstract. Infiltration is one of the major procedures in water balance research and pollution load estimation in paddy fields. In this study, a new method for indirectly estimating infiltration of paddy fields in situ was proposed and implemented in Taihu Lake basin. Since when there were no rainfall, irrigation and artificial drainage, the water depth variation process of a paddy field is only influenced by evapotranspiration and infiltration (E + F ). Firstly, (E + F ) was estimated by deciding the steady decreasing rate of water depth; then the evapotranspiration (ET) of the paddy field was calculated by using the crop coefficient method with the recommended FAO-56 Penman-Monteith equation; finally, the infiltration of the paddy field was obtained by subtracting ET from (E + F ). Results show that the mean infiltration of the studied paddy field during rice jointing-booting period was 7.41 mm day −1 , and the mean vertical infiltration and lateral seepage of the paddy field were 5.46 and 1.95 mm day −1 respectively.
A Beijing paddy field, along with in-situ experiments, was used to validate and refine the in-situ observation (IO) method to describe nonpoint source pollution (NPS) in paddy fields. Based on synchronous observed rainfall, water depth, and water quality data at two locations (1# (near inlet) and 2# (near outlet)) with large elevation differences, the evapotranspiration and infiltration loss (ET+F), runoff depth and NPS pollution load were calculated according to IO, and a common method was used to calculate ET+F. Then, the results of the different methods and locations were compared and analyzed. The results showed that 1# observation point was located at a lower position compared with 2# observation point. According to 1# observation point, there were 5 days of dry field in the drying period, which was consistent with the actual drying period, and there was a dry period of 9 days based on 2# observation point. The ET+F estimated by IO fit well with the calculated values. In the experiment, 6 overflows and 1 drainage event were identified from the observed data at locations 1# and 2#. The relative deviation of the NPS pollution of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), nitrate-nitrogen (NO3−-N) and ammonia nitrogen (NH4+-N) was between 0.6% and 2.0%. The water level gauge location had little influence on IO but mostly affected the water depth observations during the field drying period. The mareographs should be installed in low-lying paddy field areas to monitor water depth variation throughout the whole rice-growing season.
Agricultural non-point source (NPS) pollution has become a prominent problem for China’s water quality. Paddy fields pose a high risk of pollution to surrounding water bodies. The paddy in situ observation method (PIOM) can calculate the runoff pollution load of paddy fields in situ without changing the original runoff characteristics and agricultural water management measures. In this study, we carried out multisite field experiments during the rice growing period in the Taihu Lake basin and calculated the runoff pollution loads. Then, we developed a runoff pollution empirical model (RPEM) and runoff pollution machine learning models of paddy fields. Based on the PIOM, the average runoff volume was 342.1 mm, and the runoff pollution loads mainly occurred in the early-stage seedling and tillering stages. The mean TN, NH4+-N, TP and CODMn loads of paddy fields were 10.28, 3.35, 1.17 and 23.49 kg·ha−1, respectively. The mean N and P fertilizer loss rates were 4.09 and 1.95%, respectively. The RPEM mainly included the runoff model and surface water concentration model of paddy fields, the performance of which was validated based on the PIOM. The irrigation and runoff volumes of Zhoutie paddy (ZT) and Heqiao paddy (HQ) analyzed by RPEM and PIOM had an absolute difference of 1.2~3.1%. With the exception of the difference in CODMn loads of ZT, the absolute differences in TN, NH4+-N, TP and CODMn loads of ZT and HQ measured by two methods were less than 20%. This result illustrates the accuracy and feasibility of the RPEM for analysis of the water balance and runoff pollution loads of paddy fields. Based on 114 field runoff pollution datasets, RF provided the best machine learning model for TN, NH4+-N and TP, and SVM was the best model for CODMn. The training set R2 values of the best models for TN, NH4+-N and CODMn were above 0.8, and the testing set R2 values of the best models were above 0.7. The runoff pollution RF and SVM models can support the calculation and quantitative management of paddy field pollution load. This study provides a theoretical basis and technical support for the quantification of runoff pollution load and the formulation of pollution control measures in the Taihu Lake basin.
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